Understanding the association between PHC and hospital care is important for the efficient use of health care resources
, especially in rural and remote settings. This study demonstrates that too little PHC may lead to an excess of both hospitalisations and length of hospital stay, but so does too much, with people who receive either less or more than the optimal level of PHC having a marked increase in number and length of hospitalisations. The U-shape relationship is also consistent across various population sub-groups including: people over 40, females, and those with chronic conditions. These findings add to the evidence that improved access to PHC may prevent hospitalisations, improve health outcomes and lower health care costs
[3, 6]. Few studies have attempted to estimate an optimal level of medical care. Ledwidge and colleagues
 reported the required number of clinic visits was two per month to prevent hospitalisations for heart failure, while others have reported that an average of 4–5 visits a year was required to develop a sufficient knowledge base for health care continuity
. This study supports an argument that providing an optimal level of PHC in remote Indigenous communities may reduce hospitalisations, although the optimal levels of PHC service may vary with age, gender and disease.
The U-shaped distribution provides evidence for a nonlinear association between PHC activity and hospitalisation, and draws together the contradictory results of previous studies
[3, 8, 10]. A similar nonlinear pattern was also reported for the effect of distance on hospitalisation
. Lin and colleagues found the lowest hospitalisation rates among residents living between 35 and 50 kms from a hospital. Living either closer to (<35 kms) or further from (>50 kms) a hospital was associated with higher hospitalisation rates. In this study the communities were all located far from a hospital (≥87 kms). There may be a number of reasons that the PHC-hospital association varies with the level of PHC. Low levels of PHC may lead to increased false negative and delayed diagnoses, acute evacuation and hospitalisation
[30, 31]. Under this circumstance, investment in PHC can improve prompt diagnosis and treatment that may avert or postpone the need for hospital care. This inverse relationship is consistent with the majority of literature
[4–6], especially those studies undertaken in PHC shortage areas. Patients receiving PHC beyond the optimal level may be at the more severe end of clinical spectrum and require both more PHC and hospital services. In this case, PHC is not a substitute for hospital care, but a complement
. It is also possible that a portion of the extra hospitalisations are a result of increased false positive diagnoses arising from the increased PHC contacts, leading to more hospital referrals. This possibility has been recognised in previous studies
[7, 8]. Planned consultations and elective admissions tend to be positively correlated and in these cases, an expansion of PHC services may not reduce hospitalisations. There is increased heterogeneity in the distribution of results among the frequent PHC users at the right upper part of the U-curve, a group of patients with high levels of both PHC and hospital services. For this group, PHC may be insufficient for complex needs and there may be the opportunity to reduce both PHC and hospitalisations through specialised case management
Adequate PHC is considered to be essential
. The current level of access to PHC for Indigenous residents in remote areas is inadequate compared with the national average, even before consideration of the greater health need
[11, 12] and the need for culturally appropriate services
. Residents in PHC shortage areas are more likely to experience hospitalisations, and optimising PHC service levels can improve health and reduce health inequality
. PHC plays an important role in improving Indigenous health outcomes and reducing the adverse effects of health inequity, because PHC is cost-efficient for prevalent conditions
. Hypertension, chronic kidney disease, diabetes, asthma, IHD, COPD, pneumonia and urinary tract infections are common presenting problems at the NT remote clinics. Unless they progress to serious complications, such conditions are more appropriately managed by prompt interventions in PHC settings than hospitalisation.
Strengths and limitations: The strengths of this study are that for the first time, to our knowledge, the study demonstrates the U-shape association between PHC and hospital care. The methodological limitations of previous studies have been overcome by using quadratic regression models and examining routinely collected large scale service data. The spline quadratic model fits the aggregate data better than the simple quadratic model, but does so at the expense of robustness and parsimony. The spline regression model provides the advantage that, being more sensitive to the data, it is more useful when deriving vertex values. On the other hand, the simple quadratic model is more robust and more readily interpretable, making it useful for comparisons within a family of U-curves. There are also a number of limitations. Firstly, the strength of the evidence is limited by the reliability of clinic and hospital data. There is an ongoing program of consolidation and validation to maintain the quality of HRN, with the accuracy of patient demographic information in public hospital records recently reported as around 95%
. There have also been clinical audits, which have confirmed the quality of data collections
[19, 36]. Deterministic linkage is simple but considered a more reliable linkage strategy, when coding errors of HRN are minimal
. Secondly, this study did not control individual level variations and potential confounders such as types of PHC, professions of PHC providers and distance to hospital. More research is needed to further explore this topic. Multilevel analysis and multivariate adaptive regression splines may be a useful tool
. Thirdly, the study did not include people who were not recorded with either a clinic visit or hospitalisation during the study period, however the total study population was similar to the Indigenous resident population in the selected remote areas
. Additionally, PHC data were incomplete due to high population mobility, unclear clinic catchments and the availability of alternate non-DOH PHC services. While this incompleteness may lead to an underestimate of the optimal number PHC services for the population, it is unlikely to change the general pattern of the U-curve association between PHC and hospitalisations. Finally, this study is neither longitudinal nor experimental, which limits the extent to which a causal relation can be drawn and generalised. Continued recording of clinical events and the maintenance of clinical quality audits will facilitate the opportunity for longitudinal and experimental studies for this topic in the future.