Whither the roads lead to? Estimating association between urbanization and primary healthcare service use with chinese prefecture-level data in 2014

Sheng, Nong

doi:10.1371/journal.pone.0234081

Cited by 9 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, smart healthcare services could optimize allocation of resources among different medical institutions, thereby improving the availability of inpatient services and meeting the healthcare needs of patients (39). In China, most medical resources are concentrated in large hospitals owing to urbanization (40). As the expansion of hospitals cannot keep up with the growth of patients, large medical institutions face the dilemma of excessive pressure and low availability of medical resources.…”

Section: Theoretical Underpinning and Hypotheses Developmentmentioning

confidence: 99%

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Chen

Zhang

Wei

2022

Front. Public Health

View full text Add to dashboard Cite

With the emergence of the digital age, smart healthcare services based on the new generation of information technologies play an increasingly important role in improving the quality of resident health. This study empirically examined the impact of regional smart healthcare services on resident health as well as the underlying mechanism by employing a two-way fixed effects model. We constructed a Regional Smart Healthcare Service Development Index and matched it with survey data from the China Health and Retirement Longitudinal Study to validate the model. The results showed that (1) smart healthcare services have a significant positive impact on resident health. (2) The availability of outpatient services and inpatient services plays a mediating role in the relationship between regional smart healthcare services and resident health. (3) The influence of regional smart healthcare services on resident health is heterogeneous among different regions. Specifically, the effect of smart healthcare services on resident health is significant in the eastern regions, while it is not significant in the central, western, and northeastern regions. The effect of smart healthcare services on resident health is significant in rural regions but not in urban regions. This study enriches the nascent research stream of smart healthcare services. This study offers useful insights for practitioners and the government to guide them in formulating smart healthcare strategies.

show abstract

Section: Theoretical Underpinning and Hypotheses Developmentmentioning

confidence: 99%

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Chen

Zhang

Wei

2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…The first explanation could be related to the fact that patients' perceptions of the quality of medical services in DHs and high-complexity hospitals that correspond to the 2ndLC and 3rdLC are more reliable than the services offered by PHCCs at the 1stLC. 12 The DHs have basic services that include dermatology services and complementary diagnostic services for care of middle-risk pathologies and are the referral of the PHCCs, whereas hospitals at the 3rdLC have high-quality medical services with specialized equipment, attend high-risk pathologies, and are referral hospitals of the DHs. The second explanation could be the urbanization process of Formosa that began in 1947 and continues to this day, during which there was an imbalance in the population of the eastern SDs with respect to the central and western regions, bypassing the use of rural primary health-care services 12,25 and consolidating the capital city of Formosa as a services provider.…”

Section: Discussionmentioning

confidence: 99%

“…12 The DHs have basic services that include dermatology services and complementary diagnostic services for care of middle-risk pathologies and are the referral of the PHCCs, whereas hospitals at the 3rdLC have high-quality medical services with specialized equipment, attend high-risk pathologies, and are referral hospitals of the DHs. The second explanation could be the urbanization process of Formosa that began in 1947 and continues to this day, during which there was an imbalance in the population of the eastern SDs with respect to the central and western regions, bypassing the use of rural primary health-care services 12,25 and consolidating the capital city of Formosa as a services provider. Evidence of this is that the best performance of the Health System Network in the diagnosis of new cases of leprosy was and will continue to be in urban areas in general and in SDs of the eastern region of the province in particular.…”

Section: Discussionmentioning

confidence: 99%

“…11 It is possible that the urbanization process that took place in the province during the past decade and that caused the depopulation of rural areas in favor of urban ones greatly influenced the use of health-care services in general and of primary health-care services in particular. 12 On the other hand, it has also been noted that some patients seek care in major hospitals far from where they reside. 13 In this study, we hypothesized that the low detection rate of new cases of leprosy in PHCCs at the 1stLC may be the result of the bypassing PHCCs and towards DHs (2ndLC) as a result of an internal migration pattern.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of the Health System Network in Formosa, Argentina, in the Diagnosis of Leprosy

Arnaiz¹,

Abarca

Santini

et al. 2023

The American Journal of Tropical Medicine and Hygiene

View full text Add to dashboard Cite

Leprosy is a chronic, neglected tropical infectious disease, currently endemic in Formosa, a province in northwestern Argentina. To analyze the performance, distribution, and effectiveness of the health system in leprosy diagnosis in Formosa, we estimated the trend of the number of new cases of leprosy diagnosed between 2002 and 2019 and estimated a forecast for 2022 at the primary health care centers (PHCCs) of at the first level of care (1stLC), at district hospitals (DHs) of the second level of care (2ndLC), high-complexity hospitals at the third level of care (3rdLC), and in rural and urban areas. The general trend was calculated based on the new cases detection rate (NCDR) using the autoregressive–moving average model (ARMA). The 1stLC, 2ndLC, and 3rdLC and the rural/urban variables were assessed using a proportional Bayesian trend ARMA (TrARMA) model. A predictive model was used for estimated forecasts. Markov-Monte Carlo chains were applied with A Metropolis-Hastings’s algorithm. The highest median proportion (Mp) of new cases of leprosy was diagnosed at the 2ndLC (Mp, 0.67; 97.5% credibility interval [CI] [0.56–0.77]), at the 3rdLC (Mp, 0.11; 97.5% CI [0.08–0.15]), and in urban areas (urban median proportion (uMp), 0.86; 97.5% CI [0.83– 0.88]), whereas the lowest proportion of new cases was diagnosed at the 1stLC (Mp, 0.082; 97.5% CI [0.061–0.108]) and in rural areas (rural median proportion (rMp), 0.13; 97.5% CI [0.11–0.16]). Our model predicts for 2022 that a median number of new cases of leprosy of 19.70 will be diagnosed in urban areas (97.5% CI [15.94–23.80]), and will continue to be diagnosed at the 2ndLC (median number of cases, 15.33; 97.5% CI [12.40–10.52]) and 3rdLC (median number of cases, 2.43; 97.5% CI [1.97–2.94]).

show abstract

“…Furthermore, these low population–density locations usually have an increasingly aging population, demanding more health services and resources [ 44 , 45 ]. Government-supported health programs are often limited in low population–density areas because of the high rate of poverty and limited tax in such areas [ 45 ], and high population–density areas may cause congestion in major hospitals [ 46 ], suggesting a complex relationship between population density and health outcomes. The interplay between the supply and demand of health care is greatly influenced by the availability of services and the needs of the population.…”

Section: Introductionmentioning

confidence: 99%

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

Vo¹,

Tao²,

Yan³

et al. 2023

JMIR Public Health Surveill

View full text Add to dashboard Cite

Background With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions. Objective This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity. Methods This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level. Results The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis. Conclusions The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.

show abstract

Whither the roads lead to? Estimating association between urbanization and primary healthcare service use with chinese prefecture-level data in 2014

Cited by 9 publications

References 34 publications

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Performance of the Health System Network in Formosa, Argentina, in the Diagnosis of Leprosy

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

Contact Info

Product

Resources

About