Small area variation in severe, moderate, and mild anemia among women and children: A multilevel analysis of 707 districts in India

India is home to the highest global number of women and children suffering from anemia, with one in every two women impacted. India's current strategy for targeting areas with a high anemia burden is based on district-level averages, yet this fails to capture the substantial small area variation in micro-geographical (small area) units such as villages. We conducted statistical and econometric analyses to quantify the extent of small area variation in the three grades of anemia (severe, moderate, and mild) among women and children across 36 states/union territories and 707 districts of India. We utilized data from the fifth round of the National Family Health Survey conducted in 2019-21. The final analytic sample for analyses was 183,883 children aged 6-59 months and 690,153 women aged 15-49 years. The primary outcome variable for the analysis was the three anemia grades among women and children. We adopted a three-level and four-level logistic regression model to compute variance partitioning of anemia among women and children. We also computed precision-weighted prevalence estimates of women and childhood anemia across 707 districts and within-district, between-cluster variation using standard deviation (SD). For severe anemia among women, small area (villages or urban blocks) account for highest share (46.1%; Var: 0.494; SE: 0.150) in total variation followed by states (39.4%; Var: 0.422; SE: 0.134) and districts (12.8%; Var: 0.156; SE: 0.012). Similarly, clusters account for the highest share in the variation in severe (61.3%; Var: 0.899; SE: 0.069) and moderate (46.4%: Var: 0.398; SE: 0.011) anemia among children. For mild and moderate anemia among women, however, states were the highest source of variation. Additionally, we found a high and positive correlation between mean prevalence and inter-cluster SD of moderate and severe anemia among women and children. In contrast, the correlation was weaker for mild anemia among women (r = 0.61) and children (0.66). In this analysis, we are positing the critical importance of small area variation within districts when designing strategies for targeting high burden areas for anemia interventions.

Understanding Variation in Postacute Care: Developing Rehabilitation Service Areas Through Geographic Mapping

OBJECTIVE

The aims of the study were to demonstrate a method for developing rehabilitation service areas and to compare service areas based on postacute care rehabilitation admissions to service areas based on acute care hospital admissions.

DESIGN

We conducted a secondary analysis of 2013-2014 Medicare records for older patients in Texas (N = 469,172). Our analysis included admission records for inpatient rehabilitation facilities, skilled nursing facilities, long-term care hospitals, and home health agencies. We used Ward's algorithm to cluster patient ZIP Code Tabulation Areas based on which facilities patients were admitted to for rehabilitation. For comparison, we set the number of rehabilitation clusters to 22 to allow for comparison to the 22 hospital referral regions in Texas. Two methods were used to evaluate rehabilitation service areas: intraclass correlation coefficient and variance in the number of rehabilitation beds across areas.

RESULTS

Rehabilitation service areas had a higher intraclass correlation coefficient (0.081 vs. 0.076) and variance in beds (27.8 vs. 21.4). Our findings suggest that service areas based on rehabilitation admissions capture has more variation than those based on acute hospital admissions.

CONCLUSIONS

This study suggests that the use of rehabilitation service areas would lead to more accurate assessments of rehabilitation geographic variations and their use in understanding rehabilitation outcomes.

Examining the role of the physician as a source of variation: Are physician-related variations necessarily unwarranted?

RATIONALE, AIMS, AND OBJECTIVES

The physician is often implicated as an important cause of observed variations in health care service use. However, it is not clear if physician-related variation is problematic for patient care. This paper illustrates that observed physician-related variation is not necessarily unwarranted.

METHODS

This is a narrative review.

RESULTS

Many studies have attributed observed variations to the physician, but little attention is given towards discriminating between those variations that exist for good reasons and those that are unwarranted. Two arguments can be made for why physician-related variation is unwarranted. The first posits that physician-related factors should not play a role in management of care decisions because such decisions should be driven by science (which is imagined to be definitive). The second considers the possibility of supplier-induced demand as a factor driving observed variations. We show that neither argument is sufficient to rule out that physician-related variations may be warranted. Furthermore, the claim that such variations are necessarily problematic for patients has yet to be substantiated empirically.

CONCLUSIONS

It is not enough to simply show that physician-related variation can exist-one must also show where it is unwarranted and what is the magnitude of unwarranted variations. Failure to show this can have significant implications on how we interpret and respond to observed variations. Improved measurement of the sources of variation, especially with respect to patient preferences and context, may help us start to disentangle physician-related variation that is desirable from that which is unwarranted.

Effects of Geographical Accessibility on the Use of Outpatient Care Services: Quasi-Experimental Evidence from Panel Count Data

In 2010-2012, new outpatient service locations were established in Hungarian micro-regions, which had lacked such capacities before. We exploit this quasi-experiment to estimate the effect of geographical accessibility on outpatient case numbers using both individual-level and semi-aggregate panel data. We find a 24-27 per cent increase of case numbers as a result of the establishments. Our specialty-by-specialty estimates imply that a 1-min reduction of travel time to the nearest outpatient unit increases case numbers for example by 0.9 per cent in internal care and 3.1 per cent in rheumatology. The size of the new outpatient capacities has a separate effect, raising the possibility of the presence of supplier-induced demand. By combining a fixed-effects logit and a fixed-effects truncated Poisson estimator, we decompose the effects into increases in the probability of ever visiting a doctor on the one hand and an increase of the frequency of visits on the other hand. We find that new visits were dominant in the vast majority of specialties, whereas both margins were important for example in rheumatology. Finally, we demonstrate the usefulness of the fixed-effects truncated Poisson estimator in modelling count data by examining its robustness by simulations.

Racial, ethnic, and affluence differences in elderly patients' use of teaching hospitals

OBJECTIVE

To understand the role of race, ethnicity, and affluence in elderly patients' use of teaching hospitals when they have that option.

METHODS

Using a novel data set of 787,587 Medicare patients newly diagnosed with serious illness in 1993, we look at how sociodemographic factors influence whether patients use a teaching hospital for their initial hospitalization for their disease. We use hierarchical linear models to take into account differences in the availability of teaching hospitals to different groups. These models look within groups of people who live in the same county and ask what demographic factors make an individual within that county more or less likely to use a teaching hospital.

RESULTS

We find that blacks are much more likely than whites to use teaching hospitals (odds ratio [OR], 1.75; 95% confidence interval [95% CI], 1.73 to 1.77). However, Hispanics and Asian-Americans are less likely to use teaching hospitals than are whites (Hispanic OR, 0.92; 95% CI, 0.88 to 0.97; Asian-American OR, 0.89; 95% CI, 0.82 to 0.97). Medicaid patients are less likely to use teaching hospitals (given their opportunities) than are non-Medicaid recipients (OR, 0.91; 95% CI, 0.90 to 0.92). And we find a curvilinear relationship with affluence, with those in the poorest and those in the wealthiest neighborhoods most likely to use a teaching hospital.

CONCLUSION

The use of teaching hospitals is more complex that heretofore appreciated. Understanding why some groups do not go to teaching hospitals could be important for the health of those groups and of teaching hospitals.