However, agriculture continues to be the largest employer in the country. Even when workers move out of the agricultural sector, they do not always leave rural areas. The movement from an agrarian economy to a non-agrarian one in India has not been matched by the expected shift from rural areas to urban areas. Martin Salm, Email: ln. Corresponding author. Email: ln. This article has been cited by other articles in PMC. Regions compared in the decomposition analysis Appendix S2.
Details on the dataset and data cleaning process Appendix S3. Summary We assess the relative importance of demand and supply factors as determinants of regional variation in healthcare expenditures in the Netherlands. Keywords: healthcare expenditures, regional variation, the Netherlands, movers. DATA We use proprietary administrative data on annual individual healthcare expenditures by category of care included in the basic health insurance package for the period between the years and Open in a separate window.
Figure 1. Figure 2. Table 1 Summary statistics for movers and nonmovers. Figure 3. Table 3 Additive decomposition of log total healthcare expenditures. Supporting information Appendix S1. Footnotes 1 These figures are based on own calculations from our data and exclude expenditures on mental healthcare. Also, uninsured individuals are actively tracked down and fined.
As of , there were only about 28, uninsured individuals in the Netherlands Kroneman et al. Thus, we compare men women with other men women who move at different points in time and to different destinations. Region B has a large share of elderly high demand and many physicians high supply. Region C has a low share of elderly low demand and few physicians low supply.
Region D has a low share of elderly low demand and many physicians high supply. Depending on how we group these regions, we will find different demand shares. For example, if we compare Regions A and C, we will find a high demand share.
In contrast, if we compare Regions A and B, we will find a low demand share. Provinces above the 75th percentile in terms of share of elderly population are Zeeland, Limburg, and Overijssel. Finally, provinces above the 75th percentile in terms of estimated region fixed effects are Flevoland, Zeeland, and Limburg.
Provinces below the 25th percentile in terms of estimated region fixed effects are Groningen, Drenthe, and Gelderland. See Appendix S1 for the relevant maps. Such a pattern could be explained by delays in updating addresses by the insurer. However, this is unlikely in the Netherlands. Municipalities automatically forward the new address to the health insurer.
However, it is possible that people already use care in the destination region before they officially move, for example, if they move only some time after starting a job or when they have a new relationship in the destination region.
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International Journal of Health Economics and Management , 15 2 , — Sources of geographic variation in health care: Evidence from patient migration. Consequently, information on all variables was not available across all countries. Occupational status was not available for Norway and Turkey. Consequently, those aforementioned countries were excluded. Other countries were dropped for reasons explained below.
Hence, 57 countries contained data needed for this study. Three different but related measures of ill-health were used. General health complaints consisted of three items concerning bodily aches or pains, problems with sleeping and feelings of depression over the past 30 days. Disability consisted of four questions about mobility, self-care, cognition and vision over the past 30 days.
Responses were grouped to create binary outcomes. The UNEP classification used a broad grouping criterion, composed for geographical purposes, and consisting of detailed regional categories. Regions with fewer than three countries were either excluded from the analysis, or combined with an existing region within close proximity.
Countries that did not fit geographically within a region were excluded or moved to another nearby region Supplementary table A1. To evaluate if including or excluding countries that were placed in a region within close proximity altered results, a sensitivity analysis was performed that excluded China and Chad. Results were similar to those shown in table 1 , so both countries remained in the analysis.
Model 2 extended model 1 by adding female literacy and individual-level education attainment. Model 3 was the full model and included all aforementioned variables in addition to GDP and individual-level occupation. Individual-level covariates were gender, age, occupational status and educational attainment.
Occupational status was assessed using those not working, and eight white- and blue-collar functions. Educational attainment was measured in five categories from no formal schooling to post-graduate school completed. Although others exist, per capita gross domestic product GDP and female literacy are two key socio-economic factors in country development, and can be used to assess the extent to which socio-economic factors explain variations in health between regions.
For the multivariable analysis, random-intercept-only multilevel logistic models were fitted to the hierarchical data of individual respondents within countries. Multilevel logistic models allowed for the expected higher clustering of outcomes among individuals from the same country when compared to individuals with comparable covariates from other countries.
Moreover, since individual health varies between individuals and geographical location, using the multilevel approach supports the joint modelling of the outcome using explanatory variables on both the individual and country levels. In all, three phased models were fitted. Each succeeding model was an extension of the immediately preceding model, adding simultaneously both individual and contextual-level variables to the model. In model 1, we adjusted for geographical region together with individual-level age and gender, in order to explore the link between region and health.
Subsequently, in model 2, individual educational attainment and country-level female literacy were added. Model 3 extended model 2 by including individual occupation and country-level GDP. These analyses were further conducted for gender, in order to explore the associations between region and personal health for women and men separately. Finally, to examine more specific domains of personal health between regions, we repeated the third model for the other two health outcomes.
To quantify the proportion of the total outcome variance due to between-country differences, we estimated the population intraclass correlation coefficient ICC for each model, calculated as the ratio of the country-level variance to the total variance. The percentage change in country-level variance PCV of the outcome following covariate adjustment was used to gauge the percentage of country-level variance explained by the adjusted covariate s.
Results were similar to those reported in this article Supplementary table A2. Additionally, a total contextual effects model for personal health was completed Supplementary table A3 , which shows results for GDP before accounting for other contextual and individual-level socio-economic factors. All analyses were conducted in Stata Descriptive information is provided in Supplementary table A1.
Western Europeans were the highest educated, and had the highest occupational status. Table 1 lists a summary of the results for the health indicator. Model 2 findings indicated that people in Central Europe and the Former Soviet Union regions still reported significantly poorer health globally. Model 3 results showed that people in Central Europe and the Former Soviet Union regions again reported the poorest health worldwide.
The women and men in both African and South East Asian regions reported the lowest prevalence of poor health. Gender-specific models for the associations between world regional classification and individual self-rated poor health, adjusted for country-level and individual-level socio-economic factors.
Both models adjusted for world region, GDP, female literary rate, individual-level occupation and educational attainment, as well as for age categorical. For women and men this figure was 0. General health complaints were reported as very bad in 1.
Both models were adjusted for world regions, female literacy, individual-level educational attainment, GDP and individual-level occupation, as well as age categorical and gender. For the general health complaints and disability indicators this figure was 0. In the model adjusted only for age and gender, the ICC was 0.
The ICC in the final model was 0. We found associations between the regional classification used in this analysis and individual health. Socio-economic factors on both the macro and micro levels are associated with individual health, and, although results show that socio-economic factors are important, they do not fully account for all regional variations in individual health.
In multivariable adjusted analysis, participants from Central Europe and the Former Soviet Union regions are likely to report poorer health globally. This study has both strengths and limitations. A study by Hirth et al. Overall, dialysis discontinuation rates provide a measure of intensity of end-of-life care that is consistent with measures used in prior work on geographic variation. The current study also demonstrates that significant regional variation persists after adjustment for demographic differences between regions.
The documentation of significantly lower rates of dialysis discontinuation in some regions of the country does not, in itself, mean that patient care in these regions is unnecessarily aggressive or intensive. However, the regional variation documented in the current study is not an isolated finding; the pattern of regional variation in intensity of end-of-life care is consistent. Whether the question is the use of feeding tubes in elderly patients with cognitive impairment [ 11 ], nursing home residents receiving potentially burdensome medical interventions near the end of life [ 10 ], or the overall level of intensity of medical care at the end of life [ 5 ], the same regions are consistently found to provide more aggressive and expensive end-of-life care.
Future research directed at improving our understanding of the reasons why these regional variations exist is needed. Clearly, such variation cannot be explained by demographic differences between regions alone, as demonstrated by the current study. We found that, overall, the average age of incident dialysis patients rose steadily over the 15 years of the study. However, there was little change in the age at dialysis onset among patients who remained on dialysis until death, while the age at dialysis onset of those whose dialysis was eventually discontinued rose significantly.
This finding suggests that the use of dialysis in ever-older patients should be monitored closely, as co-morbidity and quality-of-life considerations are more prevalent in older patients, and contribute to many dialysis discontinuation decisions. The early start of dialysis in elderly patients, in particular, should be examined closely, as it is one of the principal factors contributing to the greying of the dialysis population, and does not consistently provide a mortality, morbidity or quality of life benefit [ 41 — 43 ].
In this study, we used the ESRD networks to examine regional variation in dialysis discontinuation rates. We recognized that the use of large geographic regions in the analysis obscured important small area variation within regions. However, we elected to use these large — mostly multi-state — regions with the goal of examining variability above the level of practice, institution, or community.
The finding of significant regional variation after adjusting for age, race and rural—urban residence is interesting because other factors that have been found to be associated with discontinuation — female sex [ 23 , 29 ], malignancy [ 23 ], dementia [ 23 ], depression [ 32 ], and pain [ 44 ] — are not likely to vary substantially from region to region. The observed variation suggests that broader cultural variables — education, religion, values, and tradition — may have to be examined to arrive at a better understanding of regional variations in care.
In efforts to rein in health care costs, policy makers will need to consider both small area variation — often reflecting local, institutional or practice patterns that lead to variation — and regional variation as documented in the current study. Future research should be directed not only at how small area differences in medical practice affect end-of-life care, but on how regional traditions and culture affect patterns and preferences for care.
Further study of regional variation in end-of-life practices may provide insights into how end-of-life care may be improved in general. Conversely, examination of communities where more aggressive end-of-life care is routine may lead to a better understanding of the patient, provider, and cultural factors that are associated with such intensive — some might say nonbeneficial — practices.
Other factors that may affect end-of-life care practices, such as provider-induced demand for services, the availability and utilization of hospice services, personal values, and religious beliefs are challenging to study, but may provide valuable insights into regional variation. The study also has several potential limitations. First, the data on discontinuation of dialysis in this study came from the death notification form utilized by the USRDS.
This form does not provide clinicians with opportunities to clarify whether dialysis was discontinued with the intention of allowing natural death to occur, or because death from another cause was imminent. The indication that dialysis therapy was discontinued prior to death does not signify that the patient died from the discontinuation of dialysis. Second, although the ESRD data reported to the USRDS is thought to be complete and current, it was not be possible to determine how many cases of ESRD deaths occurred but went unreported, and — more significantly — how many ESRD deaths were reported but without a record of a decision to terminate dialysis prior to death.
The proportion of death notification forms with known dialysis discontinuation status was homogeneous for all networks during — , but greater variation was observed before , suggesting the potential for bias due to differences either in the characteristics of patients for whom dialysis discontinuation status data was not available, or in networks with differences in reporting.
A sensitivity analyses conducted for a year cohort — compared to the overall year cohort — showed no appreciable differences between the cohorts beyond temporal influences consistent with the overall findings of the study. That is, patients in the year cohort tended to be slightly younger at the time of death, with slightly earlier withdrawal than those in the year cohort.
The year cohort, which included 5 years of earlier data, was slightly less urbanized than the year cohort, reflecting subtle demographic shifts that occurred during the observation window. The results of the multivariate analysis on the year cohort were also consistent with the outcomes of the results obtained for the full year cohort, supporting our conclusion that there is no discernible bias attributable to the differential in the reporting rates over time.
Accordingly, the effects of provider, facility and community variation and the effects of clustering of patients within regions were not addressed. The effects of such variables are best assessed using the methods that have been established for examining small area variation; this was beyond the scope of the current study.
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