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. 2021 Feb 25;16(2):e0247622. doi: 10.1371/journal.pone.0247622

Medical care needs for patients receiving home healthcare in Taiwan: Do gender and income matter?

Fang-Yi Huang 1,#, Chung-Han Ho 2,3,4,#, Jung-Yu Liao 5, Chao A Hsiung 6, Sang-Ju Yu 7, Kai-Ping Zhang 8, Ping-Jen Chen 9,10,11,*
Editor: Christy Pu12
PMCID: PMC7906386  PMID: 33630929

Abstract

Studies about medical care needs for home healthcare (HHC) previously focused on disease patterns but not gender and income differences. We used the Taiwan National Health Research Insurance Database from 1997 to 2013 to examine trends in medical care needs for patients who received HHC, and the gender and income gaps in medical care needs, which were represented by resource utilization groups (RUG). We aimed to clarify three questions: 1. Are women at a higher level of medical care needs for HHC than men, 2. Does income relate to medical care needs? 3. Is the interaction term (gender and income) related to the likelihood of medical care needs? Results showed that the highest level of medical care need in HHC was reducing whereas the basic levels of medical care need for HHC are climbing over time in Taiwan during 1998 and 2013. The percentages of women with income-dependent status in RUG1 to RUG4 are 26.43%, 26.24%, 30.68%, and 32.07%, respectively. Women were more likely to have higher medical care needs than men (RUG 3: odds ratio, OR = 1.17, 95% confidence interval, CI = 1.10–1.25; RUG4: OR = 1.13, 95% CI = 1.06–1.22) in multivariates regression test. Compared to the patients with the high-income status, patients with the income-dependent status were more likely to receive RUG3 (OR = 2.34, 95% CI = 1.77–3.09) and RUG4 (OR = 1.98, 95% CI = 1.44–2.71). The results are consistent with the perspectives of fundamental causes of disease and feminization of poverty theory, implying gender and income inequalities in medical care needs. Policymakers should increase public spending for delivering home-based integrated care resources, especially for women with lower income, to reduce the double burden of female poverty at the higher levels of medical care needs for HHC.

Introduction

Home healthcare and medical care needs

The needs for home healthcare (HHC) is increasing due to the aim of aging in place in the era of global population aging. Compared to a larger literature on the effects of social determinants on medical care needs [1–5], however, fewer have concentrated on HHC patients who live at home. Some studies investigated the association between the use of home health services, the access of device use [5], the number of readmissions [6], and the duration of rehabilitation care [7]. Little was known about specialized medical care needs such as tracheostomy and urinal catheterization care, intravenous injection, or colostomy irrigation in the HHC recipients.

Gender and medical care needs for HHC

Admittedly, HHC has undergone enormous growth over the last 30 years but studies about the relationships between gender and medical care needs for HHC service are inconclusive. Some studies found that women are more likely to receive HHC and report greater unmet home care needs than men [8, 9], and this gender gap increases by age [10]. Yet, other research reported that men presented with higher levels of need for HHC since men had higher rates of most chronic conditions, limitations in activities of daily living, and instrumental activities of daily living than women [11]. Moreover, in the patients who received HHC services, men have higher frequencies of medical care utilization than women among the disabled group [12] and also were more likely to have multiple hospitalizations in the last 3 months of life than women in people with dementia [13].

Socioeconomic status and medical care needs

The literature confirms that poverty and the level of urbanization are linked with medical care needs [14]. In general, patients with lower socioeconomic status have a lower level of medical care utilization but higher unmet needs in HHC than those with higher socioeconomic status. For instance, Saeed et al. [1] found that patients with lower incomes and living in rural area have less medical care use due to the difficulty of financial accessibility. Forbes et al. [9] also found that patients with lower socioeconomic status had more unmet home care needs than those with higher socioeconomic status since support services of HHC often are expensive, especially for people with dementia.

However, others found that socioeconomic status is not related to medical care needs. For example, Freedman et al. [5] argued that Medicare managed care enrollees with low socioeconomic status have higher access to HHC visits. Still, the minority with low income like African Americans have a greater resource utilization and greater reimbursement of home health services with increased risk for morbidity than the richer white [15]. Thus, whether the lower socioeconomic status is associated with medical care needs remaining uncertain.

Theory and arguments

Guided by the perspective of “fundamental causes of health and disease”, poorer socioeconomic status is the fundamental cause of diseases [16]. Since 1995, Bruce Link and Jo Phelan have confirmed that social conditions cause illness through two pathways. One is those with low socioeconomic status are less likely to have access to health care [17]. The other is that the mechanisms of risky health behaviors such as poor diet, stressful life, heavy smoking, less social support, and higher job hazards could lead to more anxiety, depression, higher cholesterol, blood pressure, and heart disease [18, 19]. According to the perspective of the feminization of poverty, the inequality in living standards between men and women is increasing at old age, and the majority of old women face the highest risk of poverty due to lack of income and resources [20]. Based on the theories of fundamental causes of disease and feminization of poverty [16, 20], we argued that women, patients with lower income, and those who live in more rural areas may be more likely to have a higher level of medical care needs. Hypotheses are as follows:

  1. Women are more likely to have higher levels of medical care needs in HHC than men.

  2. Lower income is more likely to be associated with higher levels of medical care needs in HHC compared to the higher income group.

  3. Women who are in income-dependent or low-income groups are more likely to be associated with higher levels of medical care needs in HHC than those with moderate income and high income.

Material and methods

Data sources and samples

The data for this cross-sectional population-based study was obtained from the National Health Insurance Research Database (NHIRD) from 1997 to 2013 in Taiwan. NHIRD is a nationwide database which contains the data of more than 23 million individuals and is managed by the National Health Research Institute (NHRI) before 2015. National Health Insurance (NHI) system, which is a single-payer universal healthcare scheme and covers 99% of the population in Taiwan [21], has been launched in 1995 [22]. We conducted a population-based study to examine the relationship between social determinants, diseases, and medical care needs levels in HHC patients. We included all patients who received HHC from 60 to 105 years old. The study was approved by the Institutional Review Board of Chi Mei Medical Center (IRB No. 10410-E01), and the requirement for informed consent was waived.

Outcome variables and covariates

The medical care needs of patients who received HHC in Taiwan are classified by resource utilization groups (RUGs) from level 1 to level 4, and HHC services at different levels of RUGs are all reimbursed with correlated payments by NHI. The first level RUG is intended for those who only require ordinary healthcare at home and level 2 to 4 of RUG are aimed at people who require one, two, three, or more specialized care services respectively. specialized care services include “change tracheostomy set”, “urinal indwelling catheterization”, “insertion of nasogastric tube”, “bladder irrigation”, “wound treatment”, “intravenous drip”, and “colostomy irrigation”. Medical care need is a nominal variable.

According to a behavioral model for explaining health care utilization developed by Andersen and Newman [23], we categorized covariates into three types, including predisposing characteristics (age and gender), enabling characteristics (income and urbanization), and need characteristics (major diseases and comorbidity). The enabling characteristics reflect the location (status) of the individuals in their society as measured by income, education, and urbanization levels of the residence. In our study, income was divided into four levels: one is the dependent group, who are usually wives, children, parents and their health insurance covered by their spouses or relatives. The other three levels of income were defined by salary-based health insurance monthly premiums, (1) low: lower than US$571 per month (New Taiwan Dollar (NT$) 20000); (2) moderate: between US$571–1141 per month (NT$20000–40000); and (3) high: US$1142 per month (NT$40001) or more. This study regarded income as one proxy of individual socioeconomic status.

Another enabling characteristic was the urbanization level of residence. The urbanization level of residence was classified into four levels of residence from urban to rural areas based on 5 indices in Taiwan: population density, percentage of residents with college-level or higher education, percentage of residents > 65 years old, percentage of residents who are agriculture workers, and the number of physicians per 100000 patients [24].

The need characteristics include comorbidities defined by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), such as cancer, neurodegenerative diseases (e.g. dementia or parkinsonism), stroke, heart failure, chronic obstructive pulmonary disease, chronic liver disease and cirrhosis, chronic kidney disease, hypertension, diabetes, coronary artery disease, hyperlipidemia, atrial fibrillation, tuberculosis, and epilepsy.

Statistical analysis

Descriptive statistics were conducted to display the distribution of the studying variables in the population sample in Table 1. Fig 1 was used to present how medical care needs changing from 1997 to 2013. In addition, χ2 analysis for categorical variables and analysis of variance (ANOVA) test for the continuous variable (age) were used to examine the differences between each independent variable and the level of RUGs. Because the RUGs were grouped into more than two categories, we adopted multinomial logistic regression to estimate the associations between variables, eight gender-income groups (an interaction term of gender multiplying income), and medical care needs as in Table 4 and Table 5. The analysis was also done for patients with cancer and stroke specifically due to their lowest and highest prevalence in major diseases served by HHC. All the analysis was conducted by STATA 13 and incorporated the weighted procedure used in the NHIRD sampling design.

Table 1. Characteristics for home healthcare patients in Taiwan from 1997 to 2013 (N = 238,176).

Variables Category Frequency Distribution (%)

  • Medical Care Needs

 RUG 1 4,491 1.89
RUG 2 116,753 49.02
RUG 3 97,710 41.02
RUG 4 19,222 8.07
Age a 79.47 b 8.37 b
Gender Male 116,547 48.93
Female 121,629 51.07
Income c Dependent 113,420 47.62
Low 80,276 33.70
Moderate 43,172 18.13
High 1,308 0.55
Urbanization Urban 122,416 51.40
Sub-urban 91,055 38.23
Sub-rural 21,018 8.82
Rural 3,687 1.55
Comorbidities
    Cancer 40,266 16.91
    Neurodegenerative diseases 74,520 31.29
    Stroke 167,070 70.15
    Heart failure 60,935 25.58
    COPD 101,848 42.76
    Chronic liver disease 45,896 19.27
    Chronic kidney disease 43,613 18.31
    Hypertension 192,983 81.03
    Diabetes 27,443 11.52
    Coronary artery disease 108,066 45.37
    Hyperlipidemia 72,766 30.55
    Atrial fibrillation 32,883 13.81
    Tuberculosis 16,400 6.89
    Epilepsy 15,157 6.36
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COPD = chronic obstructive pulmonary disease, RUG = resource utilization groups.

a Age range is from age 60 to age 105.

b The mean (standard deviation) are shown for the variables: age.

c Income dependent group refers to people whose health insurance of premiums were covered by their family members who have income. And the other three income levels were defined by salary-based health insurance premiums.

Fig 1. The change in the proportion of medical care need levels during 1997–2013.

Fig 1

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Note: 1. The total number of cases is 238,176. 2. RUG = resource utilization group.

Table 4. Multinomial logistic regression model for medical care needs levels in home healthcare (N = 238,176).

RUG2 vs. RUG1 RUG3 vs. RUG1 RUG4 vs. RUG1
Adjusted ORa (95% CI) p Adjusted ORa (95% CI) p Adjusted ORa (95% CI) p
Age 1.03 (1.03–1.04) <0.0001 1.03 (1.02–1.03) <0.0001 0.99 (0.99–0.99) <0.0001
Gender (Ref = male) 0.93 (0.87–0.99) 0.0171 1.17 (1.10–1.25) <0.0001 1.13 (1.06–1.22) 0.0004
Income (Ref = high)
    Dependent 1.75 (1.33–2.29) <0.0001 2.34 (1.77–3.09) <0.0001 1.98 (1.44–2.71) <0.0001
    Low 1.67 (1.27–2.19) 0.0002 2.18 (1.65–2.88) <0.0001 1.81 (1.32–2.48) 0.0002
    Moderate 2.09 (1.58–2.76) <0.0001 2.35 (1.77–3.13) <0.0001 1.17 (0.85–1.62) 0.3349
Urbanization (Ref = urban)
    Sub-urban 0.97 (0.91–1.03) 0.3525 1.02 (0.95–1.08) 0.6624 1.06 (0.99–1.14) 0.1133
    Sub-rural 1.12 (1.00–1.26) 0.0510 1.24 (1.1–1.39) 0.0004 1.26 (1.11–1.43) 0.0004
    Rural 1.10 (0.85–1.42) 0.4718 1.21 (0.93–1.57) 0.1495 1.16 (0.87–1.54) 0.3066
Comorbidities
    Cancer 0.88 (0.82–0.94) 0.0004 0.70 (0.65–0.75) <0.0001 0.58 (0.53–0.63) <0.0001
    Neurodegenerative diseases 1.78 (1.65–1.92) <0.0001 1.75 (1.62–1.89) <0.0001 1.43 (1.31–1.55) <0.0001
    Stroke 1.90 (1.78–2.03) <0.0001 2.43 (2.27–2.59) <0.0001 2.86 (2.66–3.08) <0.0001
    Heart failure 0.66 (0.62–0.71) <0.0001 0.62 (0.58–0.67) <0.0001 0.62 (0.57–0.67) <0.0001
    COPD 0.71 (0.67–0.76) <0.0001 0.72 (0.68–0.77) <0.0001 0.81 (0.76–0.87) <0.0001
    Chronic liver disease 1.02 (0.94–1.10) 0.6735 0.96 (0.89–1.04) 0.2787 0.85 (0.78–0.93) 0.0002
    Chronic kidney disease 1.16 (1.07–1.26) 0.0002 1.05 (0.97–1.14) 0.2120 0.92 (0.84–1.00) 0.0620
    Hypertension 1.23 (1.13–1.33) <0.0001 1.14 (1.05–1.23) 0.0014 1.00 (0.92–1.09) 0.9942
    Diabetes 1.04 (0.94–1.15) 0.4669 1.12 (1.01–1.24) 0.0299 1.11 (0.99–1.23) 0.0676
    Coronary artery disease 1.03 (0.96–1.10) 0.3877 0.98 (0.91–1.05) 0.5444 0.91 (0.84–0.98) 0.0092
    Hyperlipidemia 0.95 (0.88–1.02) 0.1307 0.86 (0.80–0.92) <0.0001 0.65 (0.60–0.70) <0.0001
    Atrial fibrillation 0.92 (0.84–1.01) 0.0665 0.89 (0.82–0.97) 0.0106 0.92 (0.84–1.02) 0.0982
    Tuberculosis 0.77 (0.70–0.86) <0.0001 0.72 (0.65–0.80) <0.0001 0.76 (0.68–0.85) <0.0001
    Epilepsy 1.35 (1.16–1.57) 0.0001 1.44 (1.24–1.68) <0.0001 1.58 (1.35–1.85) <0.0001
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CI = confidence interval, COPD = chronic obstructive pulmonary disease, OR = odds ratio, RUG = resource utilization groups.

a Adjusted for all variables in the table.

Table 5. Multinomial logistic regression model for medical care needs levels in home healthcare focusing on interaction terms of gender and income among all study subjects, cancer patients, and stroke patients.

Gender*income (Ref = male*high) RUG2 vs. RUG1 RUG3 vs. RUG1 RUG4 vs. RUG1
Adjusted ORa (95% CI) p Adjusted ORa (95% CI) p Adjusted ORa (95% CI) p
All study subjects (N = 238,176)
    Male*Dependent 1.68 (1.22–2.32) 0.0016 2.08 (1.50–2.90) <0.0001 1.69 (1.17–2.45) 0.0050
    Male*Low 1.64 (1.19–2.27) 0.0024 2.00 (1.44–2.78) <0.0001 1.64 (1.13–2.36) 0.0087
    Male*Moderate 1.88 (1.35–2.61) 0.0002 1.96 (1.40–2.75) 0.0001 1.07 (0.73–1.56) 0.7390
    Female*Dependent 1.55 (1.13–2.14) 0.0069 2.45 (1.77–3.41) <0.0001 2.02 (1.40–2.91) 0.0002
    Female*Low 1.43 (1.03–1.98) 0.0311 2.19 (1.57–3.05) <0.0001 1.73 (1.19–2.51) 0.0040
    Female*Moderate 1.99 (1.42–2.77) <0.0001 2.63 (1.87–3.70) <0.0001 1.14 (0.78–1.67) 0.4992
    Female*High 0.81 (0.45–1.46) 0.4787 0.75 (0.41–1.39) 0.3591 0.68 (0.34–1.38) 0.2825
Cancer patients (N = 40,266)
    Male*Dependent 1.51 (0.87–2.61) 0.1451 1.96 (1.10–3.48) 0.0218 1.78 (0.89–3.56) 0.1049
    Male*Low 1.52 (0.88–2.63) 0.1364 1.90 (1.07–3.37) 0.0284 1.70 (0.85–3.40) 0.1364
    Male*Moderate 1.75 (0.99–3.10) 0.0541 1.99 (1.10–3.61) 0.0230 1.23 (0.60–2.52) 0.5804
    Female*Dependent 1.54 (0.89–2.67) 0.1238 2.37 (1.34–4.20) 0.0032 2.01 (1.00–4.02) 0.0494
    Female*Low 1.33 (0.75–2.33) 0.3271 2.06 (1.14–3.70) 0.0163 1.76 (0.86–3.58) 0.1208
    Female*Moderate 2.45 (1.35–4.42) 0.0030 3.45 (1.87–6.38) <0.0001 1.66 (0.79–3.49) 0.1829
    Female*High 0.53 (0.20–1.40) 0.1977 0.61 (0.22–1.73) 0.3556 0.38 (0.08–1.74) 0.2130
Stroke patients (N = 167,070)
    Male*Dependent 1.25 (0.71–2.19) 0.4462 1.49 (0.85–2.63) 0.1674 1.22 (0.67–2.21) 0.5241
    Male*Low 1.10 (0.62–1.92) 0.7525 1.29 (0.73–2.28) 0.3782 1.08 (0.59–1.96) 0.8063
    Male*Moderate 1.42 (0.80–2.52) 0.2369 1.41 (0.79–2.52) 0.2443 0.79 (0.43–1.46) 0.4522
    Female*Dependent 1.10 (0.63–1.93) 0.7384 1.75 (1.00–3.09) 0.0518 1.48 (0.81–2.69) 0.2012
    Female*Low 1.06 (0.60–1.87) 0.8468 1.64 (0.92–2.92) 0.0908 1.34 (0.73–2.46) 0.3433
    Female*Moderate 1.43 (0.81–2.55) 0.2202 1.90 (1.06–3.40) 0.0301 0.84 (0.45–1.55) 0.5660
    Female*High 0.59 (0.22–1.60) 0.3016 0.58 (0.21–1.58) 0.2862 0.56 (0.19–1.66) 0.2944
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CI = confidence interval, OR = odds ratio, RUG = resource utilization groups.

a Adjusted for age, urbanization, comorbidities.

Results

Change of medical care needs in HHC patients over time

As shown in Fig 1, there are more and more HHC patients who have been in the second level of RUG from 1997 to 2013. The percentage of patients in RUG4 kept decreasing from 1999, while the proportion of patients with higher medical care needs (RUG 3 and RUG4) got steady during 1998 and 2002 and started to decline since 2003.

Characteristics of HHC patients

Table 1 showed patient characteristics. The percentages of the four levels of medical care need among HHC patients are 1.89%, 49.02%, 41.02%, and 8.07% respectively. The majority of patients are with the first and second levels of income: “the dependent group” (47.62%) and “low” (33.70%). Most respondents resided in very urban or urban areas. Among all HHC patients, 16.91% had cancer, 31.29% got neurodegenerative diseases, 70.15% had a stroke, 25.58% had heart failure, 42.76% got chronic obstructive pulmonary disease, 19.27% had chronic liver disease and cirrhosis, and 25.58% of the population had chronic kidney disease. These HHC patients have the highest prevalence of hypertension (81.03%) and the lowest prevalence of epilepsy (6.36%).

Variables related to medical care needs in HHC

Distributions in various variables and comorbidities for patients receiving home healthcare at different medical care need levels are shown in Table 2. The proportion of women are higher than men in groups with higher medical care needs (54.1% versus 45.9% in RUG3, 50.2% versus 49.8% in RUG4), whereas the trend of gender ratio reversed in groups with lower medical care needs. In the group with the highest level of medical care needs, the percentage of people with income-dependent status (54%) is much higher than their counterparts in other medical care needs groups.

Table 2. Distribution in age, gender, socioeconomic variables, and comorbidities for patients receiving home healthcare at different medical care need levels.

RUG 1 (n = 4,491) RUG 2 (n = 116,753) RUG 3 (n = 97,710) RUG 4 (n = 19,222)
Age (year, mean ± SD) 78.08 ±0.13 79.96±0.02 79.48 ±0.03 76.75± 0.06
Gender
    Male (%) 51.48 51.23 45.91 49.75
    Female (%) 48.52 48.77 54.09 50.25
Incomea
    Dependent (%) 45.91 45.48 48.99 54.03
    Low (%) 35.94 33.83 33.16 35.22
    Moderate (%) 16.81 20.10 17.42 10.07
    High (%) 1.34 0.60 0.43 0.68
Urbanization
    Urban (%) 52.15 51.66 50.99 51.69
    Sub-urban (%) 38.88 38.10 38.32 38.42
    Sub-rura (%) 7.59 8.70 9.09 8.50
    Rural (%) 1.38 1.54 1.60 1.39
Comorbidities
    Cancer (%) 23.20 19.08 14.93 12.26
    Neurodegenerative diseases (%) 18.62 32.27 31.82 25.60
    Stroke (%) 50.86 67.93 72.65 75.41
    Heart failure (%) 33.80 26.79 24.63 21.19
    COPD (%) 49.77 43.57 41.78 41.18
    Chronic liver disease (%) 20.82 20.34 18.58 15.89
    Chronic kidney disease (%) 18.39 19.85 17.32 13.97
    Hypertension (%) 75.40 81.84 81.04 77.32
    Diabetes (%) 10.75 11.26 11.93 11.21
    Coronary artery disease (%) 45.94 47.03 44.64 38.91
    Hyperlipidemia (%) 31.08 31.98 30.14 23.86
    Atrial fibrillation (%) 15.34 14.20 13.51 12.58
    Tuberculosis (%) 10.47 7.33 6.29 6.41
    Epilepsy (%) 4.05 5.98 6.61 8.02
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COPD = chronic obstructive pulmonary disease, RUG = resource utilization groups, SD = standard deviation. All indicate the significance p< 0.001 by χ2 test. F value is 858.80 for ANOVA test between age and RUGs.

a Income dependent group refers to people whose health insurance of premiums were covered by their family members who have income. And the other three income levels were defined by salary-based health insurance premiums.

Trends of the proportion of each medical care need group from RUG1 to RUG4 are dissimilar by the types of diseases. For instance, cancer patients and stroke patients had the lowest and highest percentages in RUG3 (14.93% versus 72.65%) and RUG4 (12.26% versus 75.41%) respectively. In addition, the percentages of cancer patients are decreasing from RUG1 to RUG4 groups (23.20%, 19.08%, 14.93%, and 12.26%) whereas the proportions of stroke patients are increasing (50.86%, 67.93%, 72.65%, and 75.41%) according to Table 2.

The relationships between gender, income, and medical care needs

Table 3 displays the association between gender-income groups and medical care need levels among all study subjects, cancer patients, and stroke patients. We found out there is a significant difference in proportions of gender-income groups among four medical care need levels of HHC. Overall, women with income-dependent status are more likely to have higher medical care needs compared to other gender-income groups. For example, the percentages of women with income-dependent status in RUG1 to RUG4 groups are 26.43%, 26.24%, 30.68%, and 32.07% respectively. At the same time, the percentages of women with high income in RUG1 to RUG4 groups are 0.38%, 0.14%, 0.10%, and 0.15%.

Table 3. The relationships between gender, income, and medical care need levels in home healthcare among all study subjects, cancer patients, and stroke patients.

All study subjects (N = 238,176) Cancer patients (N = 40,266) Stroke patients (N = 167,070)
RUG1 RUG2 RUG3 RUG4 RUG1 RUG2 RUG3 RUG4 RUG1 RUG2 RUG3 RUG4
Male & Income dependent (%) 19.46 19.23 18.30 21.94 21.21 20.13 19.56 23.50 20.27 20.91 19.38 22.79
Female & Income dependent (%) 26.45 26.26 30.69 32.09 22.46 22.47 25.14 25.67 26.88 25.66 30.74 32.07
Male & Low income (%) 22.60 22.19 20.06 22.16 25.62 25.17 23.49 25.63 24.34 22.55 20.04 22.19
Female & Low income (%) 13.34 11.64 13.10 13.06 12.09 10.04 11.28 11.50 11.56 10.46 12.26 12.36
Male & Moderate income (%) 8.46 9.36 7.22 5.11 10.27 11.29 9.60 7.30 8.10 9.66 7.24 5.17
Female & Moderate income (%) 8.35 10.73 10.20 4.96 6.24 9.96 10.23 5.43 8.01 10.20 9.93 4.76
Male & High income (%) 0.96 0.45 0.33 0.54 1.44 0.78 0.57 0.85 0.57 0.44 0.33 0.51
Female & High income (%) 0.38 0.14 0.10 0.15 0.67 0.17 0.14 0.13 0.26 0.12 0.09 0.14
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RUG = resource utilization groups, All results for χ2 tests between RUGs and eight gender-income groups are all with p values below 0.001. Income dependent group refers to people whose health insurance of premiums were covered by their family members who have income. And the other three income levels were defined by salary-based health insurance premiums.

Multinominal regression analysis for medical care needs

In Table 4, we found that women were more likely to receive RUG 3 (odds ratio, OR = 1.17, 95% confidence interval, CI = 1.10–1.25) and RUG4 (OR = 1.13, 95% CI = 1.06–1.22) than men. Compared to the patients with the high-income status, patients with the income-dependent status were more likely to receive RUG3 (OR = 2.34, 95% CI = 1.77–3.09) and RUG4 (OR = 1.98, 95% CI = 1.44–2.71). Patients with the low-income status (RUG3: OR = 2.18, 95% CI = 1.65–2.88; RUG4: OR = 1.81, 95% CI = 1.32–2.48) had similar patterns in medical care needs with the patients with income-dependent status (RUG3: OR = 2.34, 95% CI = 1.77–3.09; RUG4: OR = 1.98, 95% CI = 1.44–2.71) compared to high-income patients respectively. Moreover, the patterns of medical care needs in HHC differ by types of disease. Particularly speaking, cancer patients were less likely to have higher medical care needs, especially in RUG4 (OR = 0.58, 95% CI = 0.53–0.63). However, stroke patients were most likely to receive HHC in higher medical needs (RUG3: OR = 2.43, 95% CI = 2.27–2.59; RUG4: OR = 2.86, 95% CI = 2.66–3.08).

We further examined the associations between interaction terms of gender and income, and RUGs among all study subjects, cancer patients, and stroke patients. As shown in Table 5, among all study subjects, women with income-dependent status and low-income were more likely to receive RUG3 (OR = 2.45, 95% CI = 1.77–3.41; OR = 2.19, 95% CI = 1.57–3.05; respectively) and RUG4 (OR = 2.02, 95% CI = 1.40–2.91; OR = 1.73, 95% CI = 1.19–2.51; respectively) than men with high-income status. Compared to men with high-income status, the OR of women with income-dependent status was the highest in the RUG4 group. The patterns of gender-income effect among cancer and stroke patients were similar, however, the power of the statistical test was much weaker in stroke patients. For example, among stroke patients, the OR of women with income-dependent status and low-income status compared to men with the high-income in RUG4 was 1.48 (95% CI = 0.81–2.69) and 1.34 (95% CI = 0.73–2.46), whereas the OR of their counterparts among cancer patients in RUG4 was 2.01 (95% CI = 1.00–4.02) and 1.76 (95% CI = 0.86–3.58).

Discussion

The present study demonstrates the pattern of medical care needs in HHC in Taiwan at a national level during 1997 and 2013, which is unique compared to previous studies investigated in a shorter period [8–12]. We found that women with income-dependent status had the highest burden of medical care needs in HHC recipients in Taiwan. The finding is in keeping with the theories of fundamental causes of disease and feminization of poverty [16, 20]. Moreover, the effect of gender-income on medical care needs differs in various diseases.

In general, the highest level of medical care need in HHC was reducing whereas the basic levels of medical care need for HHC are climbing over time in Taiwan during 1998 and 2013. The trends may be explained by the dynamic equilibrium of morbidity hypothesis, suggesting the population who suffer from disease or disability with less severe conditions are increasing due to the longevity [25–30]. The onset of more burdensome symptoms is delayed in those who have already had diseases [30] and the function of vital organs get declined slowly over time [31]. The clinical improvement and healthier behavior, education, and better living environment reduce mortality rate, leading to the reduction of severity of the chronic disease, slowing its rate of progression, and minimizing the effects of severe health problems on mortality but increasing the prevalence of disease [32–35]. Thus, the number of people living with highly-morbid or severe-health conditions gets relatively constant over time, whereas, the prevalence of people with mild and moderate disability or non-lethal impairment increases [29, 36–39].

Despite several medical and social elucidations that have been articulated in attempts to account for the differences in medical care needs for HHC, how gender and income influence this association remains unclear, especially for the older population [10, 12]. In our study, an increasing proportion of women was noted in the groups with higher medical care needs. It could be because women suffer more from nonlethal illness, especially in a higher rate of chronic debilitation disorders [40], and have better longevity than men, leading to their higher medical care needs. In particular, women in the income-dependent group have much higher percentages in the groups with higher medical care needs than women in other income groups, while the proportion of men with income-dependent status low income is lower than men with low income. The findings confirmed our hypotheses 1 and 2 and echoed the theories of fundamental causes of disease and feminization of poverty [16, 20].

Given the remarkable gender and income gaps in medical care needs, the impact of interaction terms of gender and income has been further established in our study. Women with income-dependent status or low-income are significantly associated with a higher level of medical care needs than men with high-income, corroborating Hypothesis 3. Moreover, different diseases may have impacts on the gender-income gaps in medical care needs. For example, similar patterns were found in all study subjects or in cancer patients that women with lower income status were more likely to receive higher levels of medical care needs compared to their male counterparts. However, the patterns are less significant in stroke patients across the different gender-income status. The difference may be related to the higher prevalence rate and severity of male in stroke patients [41]. More research is needed to further examine the influences of gender and income in disease-specific HHC.

Given the population aging and increased longevity globally, the expanding numbers of the elderly may not only lead to decreasing social benefits per capita but also intensify the healthcare cost [3, 42]. The demand for long-term care expenditures and total out-of-pocket spending may exacerbate the inequity due to gender and income. Previous studies showed that people receiving HHC are at high risk of acute hospitalization, emergency room visit, and other acute healthcare utilization, especially for those with high medical care needs [12, 43]. Our findings suggested an increase in public spending for delivering proper preventive, supportive, and therapeutic care for women with income-dependent status or low-income may be crucial. A home-based intervention that integrates health and social care support, or a timely program coordinated with elderly care specialists for maximizing recipients’ independence at home may help to reduce the transitions to medical care in higher-cost settings [44, 45].

Limitations and strengths

Due to the cross-sectional design, the study cannot reveal any causal inferences but only the associations between social determinants and medical care needs. Second, since the NHIRD is a claim-based routinely collected dataset, it does not provide some specific socioeconomic data such as education years, out-of-pocket expense for healthcare, or other economic circumstances. Third, the evaluation of medical care needs was based on the need for special medical care items and on the time-point when patients received HHC for the first time. The change in medical care needs cannot be identified in our study.

Conclusion

The secular trend of the changing proportions of medical care need levels in HHC in Taiwan fitted the dynamic equilibrium of morbidity hypothesis, put forward by Manton. Lower socioeconomic status can partially explain the higher level of medical care need among women who received HHC in Taiwan, which is consistent with the theories of fundamental causes of disease and feminization of poverty. To reduce the inequity in medical care needs due to gender and income, policymakers could allocate timely integrated care resources for women in lower-income status at home. Further study is needed for exploring the effect of gender-income on medical care needs in different diseases.

Acknowledgments

We would like to thank Professor Elizabeth L. Sampson and Professor Irene Petersen at University College London for their advice on the study approach and analysis.

Data Availability

The data for this study was obtained from the National Health Insurance Research Database (NHIRD, http://nhird.nhri.org.tw/). The application for permission to access the data was sent from Chi-Mei Medical Center to National Health Research Institutes (NHRI), Taiwan and approved. But restrictions apply to the availability of these data, which were used under license for the Chi-Mei Medical Center and current study only, and so are not publicly available. Data are however can be checked for any researcher who may concern about its reliability upon reasonable request to the Department of Research or Institutional Review Board in Chi-Mei Medical Center, Taiwan (https://www.chimei.org.tw/main/cmh_department/top/54000_index.html). For further analyses of these data, researchers should apply for permission independently from the NHIRD, which has been transferred to the Health and Welfare Data Science Center (HWDC), Department of Statistics, Ministry of Health and Welfare, Taiwan (http://dep.mohw.gov.tw/DOS/np-2497-113.html).

Funding Statement

The project was funded by the Chi-Mei Medical Center (grant reference: CMHCR11009), and Ping-Jen Chen was supported by a Government Scholarship for Overseas PhD Study, Ministry of Education, Taiwan (grant reference: 1051165-1-UK-002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Christy Pu

9 Dec 2020

PONE-D-20-32788

Medical care needs for patients receiving home healthcare in Taiwan: Do gender and income matter?

PLOS ONE

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Reviewer #1: Lines 60-61 - Please rephrase to specify the following:-

1. Was hospitalisation sought for mental healthcare?

2. Was over the counter medication used for mental healthcare?

If not, then the type of conditions for which these services were availed should be mentioned.

Lines 128-130 - Is the use of the term 'factors' methodologically significant here?

If the authors carried out a factor analysis of the independent variables to arrive at the three factors, it should be mentioned and a justification should be given for the same.

If this is a theoretical categorisation of the independent variables, it should be specified.

Lines 131-132 - Please rephrase for clarity.

Please explain in detail how the 'aged-income' groups were created.

Is this is an interaction term? If yes, is it additive or multiplicative? How was this determined?

This should either be explained in detail or a reference should be cited where this technique has been applied.

Line 158 - Please refer to the comment on lines 131-132.

Lines 163-164 - Please rephrase for clarity.

Lines 164-166 - What is the relevance of this sentence here?

Section - Statistical Analysis

There is no mention of descriptive statistics to showcase the distribution of the variables in the population."

Lines 162-163 - Please demonstrate how this was carried out with an example of an independent variable and a mediating and/or a moderating variable.

Table 1 - Please leave this column blank for the disease conditions.

Line 201 - Increasing how? Across time?

Lines 207-208 - This appears to be counter-intuitive, since kidney disease would require more complex care.

Lines 237-238 - What level of medical care needs?

Table 3 - Why are medical care needs not disaggregated by RUG levels?

Line 258 - Please change to income dependent as 'poorer income' can refer to both low-income and dependent individuals.

Lines 254-256 and Table 4 - Please explain the rationale for grouping disease conditions into 'All' and 'Cancer'. Does the category 'All' include cancers?

Table 4 - The title should include 'cancer' since no other disease conditions are being referenced in the table.

Table 4 Note 2 - Where are the p-values shown?

Table 4 - Please regroup columns for clarity. It would be better to group all the RUG levels for all-cause care needs together, and for cancer separately.

Line 289 - Please specify 'income dependent women'.

Lines 289-290 - Rephrase and add information for clarity and correctness.

According to the OR for interaction terms in Table 3, income-dependent women have a higher likelihood of having higher levels of medical care needs, that is, a higher RUG level.

The same is borne out by the results shown in Table 4, where it is seen that the group 'female and income-dependent' has the highest value in every RUG level, for all-cause medical care needs. Similarly, the percentage of female income-dependent individuals shows an increasing trend from RUG1 to RUG2.

Lines 292-293 - Please provide a reference.

Lines 298-299 - Please rephrase for clarity.

Line 314 - Please check this. Should this be similar?

Lines 316-317 - Please rephrase for clarity

Lines 320-322 - Please specify the income groups accurately and uniformly. Throughout the text, the terms used are 'low-income' and 'income-dependent'. 'Poor income' could be incorrectly interpreted as a combination of both.

Lines 325-328 - Please rephrase for clarity.

Lines 347-349 - Please mention why this is essential and what could be the result if such measures are not taken.

Describe in terms of premature mortality, increased out-of-pocket expenditure for households, and costs to the system related to income loss and disability.

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PLoS One. 2021 Feb 25;16(2):e0247622. doi: 10.1371/journal.pone.0247622.r002

Author response to Decision Letter 0

22 Jan 2021

Response to reviewers’ comments:

Reviewer 1

1. Typo and grammar issues

Author reply: All typo and grammar issues have been revised

2. Lines 60-61 - Please rephrase to specify the following:

a. Was hospitalisation sought for mental healthcare?

b. Was over the counter medication used for mental healthcare?

If not, then the type of conditions for which these services were availed should be mentioned.

Author reply: Thank you for the comment. We have updated the reference and added more content to clearly describe the type of conditions for which these services were availed, and the content mentioned in the revised manuscript and shown below:

Introduction (page 3, line 69-72)

“Some studies found that women are more likely to receive HHC and report greater unmet home care needs than men (8,9), and this gender gap increases by age (10). Yet, other research reported that men presented with higher levels of need for HHC since men had higher rates of most chronic conditions, limitations in activities of daily living, and instrumental activities of daily living than women (11). Moreover, in the patients who received HHC services, men have higher frequencies of medical care utilization than women among the disabled group (12) and also were more likely to have multiple hospitalizations in the last 3 months of life than women in people with dementia (13).”

3. Lines 128-130 - Is the use of the term 'factors' methodologically significant here?

If the authors carried out a factor analysis of the independent variables to arrive at the three factors, it should be mentioned and a justification should be given for the same.

If this is a theoretical categorisation of the independent variables, it should be specified.

Author reply: Thank you for the comment. This is actually a theoretical categorization of the independent variables and we have updated the reference and revised the content. The content mentioned in the revised manuscript is shown below:

Material and methods (page 6, line 133-136)

“According to a behavioral model for explaining health care utilization developed by Andersen and Newman (23), we categorized covariates into three types, including predisposing characteristics (age and gender), enabling characteristics (income and urbanization), and need characteristics (major diseases and comorbidity).”

4. Lines 131-132 - Please rephrase for clarity.

Please explain in detail how the 'aged-income' groups were created.

Is this is an interaction term? If yes, is it additive or multiplicative? How was this determined?

This should either be explained in detail or a reference should be cited where this technique has been applied.

Author reply: Thank you for the comment. The 'aged-income' was a typo. The correct one is the 'gender-income' group. We have changed the 'aged-income' groups into the 'gender-income' groups. The definition of income levels is mentioned on Page 6 Line 138-144 in the revised manuscript

We have rearranged the sequence of tables to demonstrate our reasoning more clearly. Due to the specific research interest in gender and income, we present the descriptive data of the pattern of medical care needs for subjects categorized in different gender-income groups in Table 3 (page 13).

Given the significant roles of gender and income levels in medical care needs in multivariate analysis (Table 4), we further generate interaction terms “Gender*Income”, which includes eight gender-income categories, and test their effect in the multinominal regression model (Table 5). The interaction term is multiplicative and the reference group is male with high-income in Table 5.

5. Section - Statistical Analysis

There is no mention of descriptive statistics to showcase the distribution of the variables in the population.

Author reply: Thank you for the comment. We have revised the content, which is mentioned in the revised manuscript and shown below:

Statistical analysis (page 7, line 157-161)

“Descriptive statistics were conducted to display the distribution of the studying variables in the population sample in Table 1. Figure 1 was used to present how medical care needs changing from 1997 to 2013. In addition, χ2 analysis for categorical variables and analysis of variance (ANOVA) test for the continuous variable (age) were used to examine the differences between each independent variable and the level of RUGs.”

6. Line 158 - Please refer to the comment on lines 131-132.

Author reply: Thank you for the comment. The 'aged-income' groups were a typo. The correct one is the 'gender-income' groups. We have changed the 'aged-income' groups into the 'gender-income' groups. The definition of income levels is mentioned on Page 6 Line 138-144 in the revised manuscript

7. Lines 162-163 - Please demonstrate how this was carried out with an example of an independent variable and a mediating and/or a moderating variable.

Lines 163-164 - Please rephrase for clarity.

Lines 164-166 - What is the relevance of this sentence here?

Author reply: Thank you for the comment. We have performed a new multinominal regression test to replace the previous analysis and revised the content, which is mentioned in the revised manuscript and shown below:

Statistical analysis (page 7, line 161-166)

“Because the RUGs were grouped into more than two categories, we adopted multinomial logistic regression to estimate the associations between variables, eight gender-income groups (an interaction term of gender multiplying income), and medical care needs as in Table 4 and Table 5. The analysis was also done for patients with cancer and stroke specifically due to their lowest and highest prevalence in major diseases served by HHC.”

The example of moderating may be shown in the new Table 5 and the revised content is mentioned in the revised manuscript and shown below:

Results (page 15, line 261-267)

”The patterns of gender-income effect among cancer and stroke patients were similar, however, the power of the statistical test was much weaker in stroke patients. For example, among stroke patients, the OR of women with income-dependent status and low-income status compared to men with the high-income in RUG4 was 1.48 (95% CI= 0.81-2.69) and 1.34 (95% CI= 0.73-2.46), whereas the OR of their counterparts among cancer patients in RUG4 was 2.01 (95% CI= 1.00-4.02) and 1.76 (95% CI= 0.86-3.58).”

8. Table 1 - Please leave this column blank for the disease conditions.

Author reply: Thank you for the comment. We have left this column blank for the disease conditions.

9. Line 201 - Increasing how? Across time?

Author reply: Thank you for the comment. We have revised the content, which is mentioned in the revised manuscript and shown below:

Results (page 10, line 203-208)

“The proportion of women are higher than men in groups with higher medical care needs (54.1% versus 45.9% in RUG3, 50.2% versus 49.8% in RUG4), whereas the trend of gender ratio reversed in groups with lower medical care needs. In the group with the highest level of medical care needs, the percentage of people with income-dependent status (54%) is much higher than their counterparts in other medical care needs groups.”

10. Lines 207-208 - This appears to be counter-intuitive, since kidney disease would require more complex care.

Author reply: Thank you for the comment. We have deleted the confusing interpretation in the previous manuscript and focused on the trends of the proportion in each medical care need group among different types of diseases. The trends of the percentages from RUG1 to RUG4 groups are decreasing among major diseases, such as cancer, heart failure, or chronic liver disease, could be interpreted as patients with aforementioned diseases tend to seek medical care in hospital rather than in upgraded home healthcare. The revised content is mentioned in the revised manuscript and shown below:

Results (page 10, line 209-214)

“Trends of the proportion of each medical care need group from RUG1 to RUG4 are dissimilar by the types of diseases. For instance, cancer patients and stroke patients had the lowest and highest percentages in RUG3 (14.93% versus 72.65%) and RUG4 (12.26% versus 75.41%) respectively. In addition, the percentages of cancer patients are decreasing from RUG1 to RUG4 groups (23.20%, 19.08%, 14.93%, and 12.26%) whereas the proportions of stroke patients are increasing (50.86%, 67.93%, 72.65%, and 75.41%) according to Table 2.”

11. Lines 237-238 - What level of medical care needs?

Table 3 - Why are medical care needs not disaggregated by RUG levels?

Author reply: Thank you for the comment. We have replaced multi-ordinal regression models (Table 3 in the previous manuscript) with multi-nominal regression models (Table 4 and 5 in the revised manuscript), and rewritten the whole paragraphs which are mentioned in the revised manuscript and shown below:

Results (page 14-15, line 239-267)

“Multinominal regression analysis for medical care needs

In Table 4, we found that women were more likely to receive RUG 3 (odds ratio, OR= 1.17, 95% confidence interval, CI= 1.10-1.25) and RUG4 (OR= 1.13, 95% CI= 1.06-1.22) than men. Compared to the patients with the high-income status, patients with the income-dependent status were more likely to receive RUG3 (OR= 2.34, 95% CI= 1.77-3.09) and RUG4 (OR= 1.98, 95% CI= 1.44-2.71). Patients with the low-income status (RUG3: OR= 2.18, 95% CI= 1.65-2.88; RUG4: OR= 1.81, 95% CI= 1.32-2.48) had similar patterns in medical care needs with the patients with income-dependent status (RUG3: OR= 2.34, 95% CI= 1.77-3.09; RUG4: OR= 1.98, 95% CI= 1.44-2.71) compared to high-income patients respectively. Moreover, the patterns of medical care needs in HHC differ by types of disease. Particularly speaking, cancer patients were less likely to have higher medical care needs, especially in RUG4 (OR= 0.58, 95% CI= 0.53-0.63). However, stroke patients were most likely to receive HHC in higher medical needs ( RUG3: OR= 2.43, 95% CI= 2.27-2.59; RUG4: OR= 2.86, 95% CI= 2.66-3.08).

We further examined the associations between interaction terms of gender and income, and RUGs among all study subjects, cancer patients, and stroke patients. As shown in Table 5, among all study subjects, women with income-dependent status and low-income were more likely to receive RUG3 (OR= 2.45, 95% CI= 1.77-3.41; OR= 2.19, 95% CI= 1.57-3.05; respectively) and RUG4 (OR= 2.02, 95% CI= 1.40-2.91; OR= 1.73, 95% CI= 1.19-2.51; respectively) than men with high-income status. Compared to men with high-income status, the OR of women with income-dependent status was the highest in the RUG4 group. The patterns of gender-income effect among cancer and stroke patients were similar, however, the power of the statistical test was much weaker in stroke patients. For example, among stroke patients, the OR of women with income-dependent status and low-income status compared to men with the high-income in RUG4 was 1.48 (95% CI= 0.81-2.69) and 1.34 (95% CI= 0.73-2.46), whereas the OR of their counterparts among cancer patients in RUG4 was 2.01 (95% CI= 1.00-4.02) and 1.76 (95% CI= 0.86-3.58).”

Table 4 (page 16-17)

Table 5 (page 18-19)

12. Lines 254-256 and Table 4 - Please explain the rationale for grouping disease conditions into 'All' and 'Cancer'. Does the category 'All' include cancers?

Author reply: Thank you for the comment. The category 'All' includes cancer and stroke patients. We have added the rationale of doing the analysis not only in all study subjects but also in cancer and stroke patients individually. The revised content is mentioned in the revised manuscript and shown below:

Statistical analysis (page 7, line 164-166)

“The analysis was also done for patients with cancer and stroke specifically due to their lowest and highest prevalence in major diseases served by HHC.”

Results (page 12, line 225-233)

“The relationships between gender, income, and medical care needs

Table 3 displays the association between gender-income groups and medical care need levels among all study subjects, cancer patients, and stroke patients. We found out there is a significant difference in proportions of gender-income groups among four medical care need levels of HHC. Overall, women with income-dependent status are more likely to have higher medical care needs compared to other gender-income groups. For example, the percentages of women with income-dependent status in RUG1 to RUG4 groups are 26.43%, 26.24%, 30.68%, and 32.07% respectively. At the same time, the percentages of women with high income in RUG1 to RUG4 groups are 0.38%, 0.14%, 0.10%, and 0.15%.”

Table 3 (page 13)

13. Line 258 - Please change to income-dependent as 'poorer income' can refer to both low-income and dependent individuals.

Line 259 - 'However' should be replaced by 'At the same time' because the following finding is not contradictory to the one preceding it.

Author reply: Thank you for the comment. We have changed “poorer income” to “income-dependent status” and also revised the similar confusing words in all the text. We have replaced ‘However’ with ‘At the same time’.

14. Table 4 - The title should include 'cancer' since no other disease conditions are being referenced in the table.

Table 4 - Please regroup columns for clarity. It would be better to group all the RUG levels for all-cause care needs together, and for cancer separately.

Table 4 Note 2 - Where are the p-values shown?

Author reply: Thank you for the comment. We have revised the title and content in the table and moved its sequence to Table 3, which is mentioned in the revised manuscript and shown below:

Table 3 (page 13)

Line 225-233 “Table 3. The relationships between gender, income, and medical care need level in home healthcare among all study subjects, cancer patients, and stroke patients”

Line 236 “All results for χ2 tests between RUGs and eight gender-income groups are all with p values below 0.001.”

15. Line 289 - Please specify 'income dependent women'.

Lines 289-290 - Rephrase and add information for clarity and correctness.

Author reply: Thank you for the comment. We have rewritten the paragraph, which is mentioned in the revised manuscript and shown below:

Discussion (page 20, line 275-281)

“The present study demonstrates the pattern of medical care needs in HHC in Taiwan at a national level during 1997 and 2013, which is unique compared to previous studies investigated in a shorter period (8–12). We found that women with income-dependent status had the highest burden of medical care needs in HHC recipients in Taiwan. The finding is in keeping with the theories of fundamental causes of disease and feminization of poverty (16,20). Moreover, the effect of gender-income on medical care needs differs in various diseases.”

16. Lines 292-293 - Please provide a reference

Author reply: Thank you for the comment. We have provided references in the text.

17. Lines 298-299 - Please rephrase for clarity.

Author reply: Thank you for the comment. We have rephrased the whole paragraph, which is mentioned in the revised manuscript and shown below:

Discussion (page 20, line 282-294)

“In general, the highest level of medical care need in HHC was reducing whereas the basic levels of medical care need for HHC are climbing over time in Taiwan during 2005 and 2013. The trends may be explained by the dynamic equilibrium of morbidity hypothesis, suggesting the population who suffer from disease or disability with less severe conditions are increasing due to the longevity (26–30). The onset of more burdensome symptoms is delayed in those who have already had diseases (30) and the function of vital organs get declined slowly over time (31). The clinical improvement and healthier behavior, education, and better living environment reduce mortality rate, leading to the reduction of severity of the chronic disease, slowing its rate of progression, and minimizing the effects of severe health problems on mortality but increasing the prevalence of disease (32–35). Thus, the number of people living with highly-morbid or severe-health conditions gets relatively constant over time, whereas, the prevalence of people with mild and moderate disability or non-lethal impairment increases (29,36–39).”

18. Line 314 - Please check this. Should this be similar?

Lines 316-317 - Please rephrase for clarity

Lines 320-322 - Please specify the income groups accurately and uniformly. Throughout the text, the terms used are 'low-income' and 'income-dependent'. 'Poor income' could be incorrectly interpreted as a combination of both.

Lines 325-328 - Please rephrase for clarity.

Author reply: Thank you for the comment. We have made the terms 'low-income' and 'income-dependent' consistently and rephrased the whole paragraphs, which is mentioned in the revised manuscript and shown below:

Discussion (page 20-21, line 295-316)

“Despite several medical and social elucidations that have been articulated in attempts to account for the differences in medical care needs for HHC, how gender and income influence this association remains unclear, especially for the older population (10,12). In our study, an increasing proportion of women was noted in the groups with higher medical care needs. It could be because women suffer more from nonlethal illness, especially in a higher rate of chronic debilitation disorders (40), and have better longevity than men, leading to their higher medical care needs. In particular, women in the income-dependent group have much higher percentages in the groups with higher medical care needs than women in other income groups, while the proportion of men with income-dependent status low income is lower than men with low income. The findings confirmed our hypotheses 1 and 2 and echoed the theories of fundamental causes of disease and feminization of poverty (16,20).

Given the remarkable gender and income gaps in medical care needs, the impact of interaction terms of gender and income has been further established in our study. Women with income-dependent status or low-income are significantly associated with a higher level of medical care needs than men with high-income, corroborating Hypothesis 3. Moreover, different diseases may have impacts on the gender-income gaps in medical care needs. For example, similar patterns were found in all study subjects or in cancer patients that women with lower income status were more likely to receive higher levels of medical care needs compared to their male counterparts. However, the patterns are less significant in stroke patients across the different gender-income status. The difference may be related to the higher prevalence rate and severity of male in stroke patients (41). More research is needed to further examine the influences of gender and income in disease-specific HHC.”

19. Lines 347-349 - Please mention why this is essential and what could be the result if such measures are not taken.

Describe in terms of premature mortality, increased out-of-pocket expenditure for households, and costs to the system related to income loss and disability.

Author reply: Thank you for the comment. We have added a paragraph that how the policy change is needed, which is mentioned in the revised manuscript and shown below:

Discussion (page 21-22, line 317-328)

“Given the population aging and increased longevity globally, the expanding numbers of the elderly may not only lead to decreasing social benefits per capita but also intensify the healthcare cost (3,42). The demand for long-term care expenditures and total out-of-pocket spending may exacerbate the inequity due to gender and income. Previous studies showed that people receiving HHC are at high risk of acute hospitalization, emergency room visit, and other acute healthcare utilization, especially for those with high medical care needs (12,43). Our findings suggested an increase in public spending for delivering proper preventive, supportive, and therapeutic care for women with income-dependent status or low-income may be crucial. A home-based intervention that integrates health and social care support, or a timely program coordinated with elderly care specialists for maximizing recipients’ independence at home may help to reduce the transitions to medical care in higher-cost settings (44,45).”

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Decision Letter 1

Christy Pu

10 Feb 2021

Medical care needs for patients receiving home healthcare in Taiwan: do gender and income matter?

PONE-D-20-32788R1

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Acceptance letter

Christy Pu

17 Feb 2021

PONE-D-20-32788R1

Medical care needs for patients receiving home healthcare in Taiwan: do gender and income matter?

Dear Dr. Chen:

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on behalf of

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Associated Data

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    Supplementary Materials

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    Data Availability Statement

    The data for this study was obtained from the National Health Insurance Research Database (NHIRD, http://nhird.nhri.org.tw/). The application for permission to access the data was sent from Chi-Mei Medical Center to National Health Research Institutes (NHRI), Taiwan and approved. But restrictions apply to the availability of these data, which were used under license for the Chi-Mei Medical Center and current study only, and so are not publicly available. Data are however can be checked for any researcher who may concern about its reliability upon reasonable request to the Department of Research or Institutional Review Board in Chi-Mei Medical Center, Taiwan (https://www.chimei.org.tw/main/cmh_department/top/54000_index.html). For further analyses of these data, researchers should apply for permission independently from the NHIRD, which has been transferred to the Health and Welfare Data Science Center (HWDC), Department of Statistics, Ministry of Health and Welfare, Taiwan (http://dep.mohw.gov.tw/DOS/np-2497-113.html).

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