Mining care trajectories using health administrative information systems: the use of state sequence analysis to assess disparities in prenatal care consumption

Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis

BACKGROUND

In standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest.

METHODS

We introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients.

RESULTS

The procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment.

CONCLUSIONS

Investigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.

Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review

BACKGROUND

Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision support. These pathways can be discovered from existing healthcare data, in particular with machine learning which is a family of methods used to learn patterns from data. This review provides a comprehensive overview of the literature concerning the use of machine learning methods for clinical pathway discovery from patient data.

METHODS

Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method , we conducted a systematic review of the existing literature. We searched 6 databases, i.e., ACM Digital Library, ScienceDirect, Web of Science, PubMed, IEEE Xplore, and Scopus spanning from January 2004 to December 2023 using search terms pertinent to clinical pathways and their development. Subsequently, the retrieved papers were analyzed to assess their relevance to the scope of this study.

RESULTS

In total, 131 papers that met the specified inclusion criteria were identified. These papers expressed diverse motivations behind data-driven clinical pathway discovery ranging from knowledge discovery to conformance checking with established clinical guidelines (derived from existing literature and clinical experts). Notably, the predominant methods employed (67.2%, n=88) involved unsupervised machine learning techniques, such as clustering and process mining.

CONCLUSIONS

Relevant clinical pathways can be discovered from patient data using machine learning methods, with the desirable potential to aid clinical decision-making in healthcare. However, to reach this objective, the methods used to discover pathways should be reproducible, and rigorous performance evaluation by clinical experts needs to be conducted for validation.

Exploring heterogeneity in coxarthrosis medication use patterns before total hip replacement: a State Sequence Analysis

OBJECTIVE

Evidence of geographical variation in total hip replacement (THR) and deviations from treatment guidelines persists. In this exploratory study, we aim to gain an in-depth understanding of patients' healthcare trajectories by identifying and visualising medication use patterns in coxarthrosis patients before surgery. We examine their association with patient characteristics and THR, and compare them with recommendations on mild analgesics, opioid prescription and exhaustion of conservative therapy.

METHODS

In this exploratory study, we apply State Sequence Analysis (SSA) on German health insurance data (2012-2015). We analyse a cohort of coxarthrosis patients, half of whom underwent THR after a 1 year observation period and half of whom did not undergo surgery until at least 1 year after the observation period. Hierarchical states are defined based on prescriptions. We construct sequences, calculate sequence similarity using optimal matching and identify medication use patterns via clustering. Patterns are visualised, descriptive statistics are presented and logistic regression is employed to investigate the association of medication patterns with subsequent THR.

RESULTS

Seven distinct medication use patterns are identified, correlating strongly with patient characteristics and subsequent THR. Two patterns leading to THR demonstrate exhaustion of pharmacological therapy. Opioid use is concentrated in two small patterns with low odds for THR. The most frequent pattern lacks significant pharmacological therapy.

CONCLUSIONS

This SSA uncovers heterogeneity in medication use patterns before surgery in coxarthrosis patients. Cautious opioid handling and adherence to a stepped prescription approach are observed, but many patients display low medication therapy usage and lack evidence of exhausting conservative options before surgery.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3 years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

Applying sequence analysis to uncover 'real-world' clinical pathways from routinely collected data: a systematic review

OBJECTIVES

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.

STUDY DESIGN AND SETTING

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.

RESULTS

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.

CONCLUSION

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

What happens after early intervention? Mapping early intervention in psychosis care pathways in the 12 months after discharge

AIM

Early intervention services are the established and evidence-based treatment option for individuals with first-episode psychosis. They are time-limited, and care pathways following discharge from these services have had little investigation. We aimed to map care pathways at the end of early intervention treatment to determine common trajectories of care.

METHODS

We collected health record data for all individuals treated by early intervention teams in two NHS mental health trusts in England. We collected data on individuals' primary mental healthcare provider for 52 weeks after the end of their treatment and calculated common trajectories of care using sequence analysis.

RESULTS

We identified 2224 eligible individuals. For those discharged to primary care we identified four common trajectories: Stable primary care, relapse and return to CMHT, relapse and return to EIP, and discontinuity of care. We also identified four trajectories for those transferred to alternative secondary mental healthcare: Stable secondary care, relapsing secondary care, long-term inpatient and discharged early. The long-term inpatient trajectory (1% of sample) accounted for 29% of all inpatient days in the year follow-up, with relapsing secondary care (2% of sample and 21% of inpatient days), and Relapse and return to CMHT (5% of sample, 15% of inpatient days) the second and third most frequent.

CONCLUSIONS

Individuals have common care pathways at the end of early intervention in psychosis treatment. Understanding common individual and service features that lead to poor care pathways could improve care and reduce hospital use.

Discovering patterns in outpatient neurology appointments using state sequence analysis

BACKGROUND

Outpatient services in the UK, and in particular outpatient neurology services, are under considerable pressure with an ever-increasing gap between capacity and demand. To improve services, we first need to understand the current situation. This study aims to explore the patterns of appointment type seen in outpatient neurology, in order to identify potential opportunities for change.

METHODS

We use State Sequence Analysis (SSA) on routinely collected data from a single neurology outpatient clinic. SSA is an exploratory methodology which allows patterns within sequences of appointments to be discovered. We analyse sequences of appointments for the 18 months following a new appointment. Using SSA we create groups of similar appointment sequence patterns, and then analyse these clusters to determine if there are particular sequences common to different diagnostic categories.

RESULTS

Of 1315 patients 887 patients had only one appointment. Among the 428 patients who had more than one appointment a 6 monthly cycle of appointments was apparent. SSA revealed that there were 11 distinct clusters of appointment sequence patterns. Further analysis showed that there are 3 diagnosis categories which have significant influence over which cluster a patient falls into: seizure/epilepsy, movement disorders, and headache.

CONCLUSIONS

Neurology outpatient appointment sequences show great diversity, but there are some patterns which are common to specific diagnostic categories. Information about these common patterns could be used to inform the structure of future outpatient appointments.

Analytical methods for identifying sequences of utilization in health data: a scoping review

BACKGROUND

Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.

METHOD

This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.

RESULTS

The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.

CONCLUSION

Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.

End-of life medical spending and care pathways in the last 12 months of life: A comprehensive analysis of the national claims database in France

BACKGROUND

To inform policy makers on efficient provision of end-of-life care, we estimated the 12-month medical expenditures of French decedents in 2015.

METHODS

We estimated total medical expenditures by service type and diagnosis category, and analyzed care pathways for breast cancer, dementia, chronic obstructive lung disease.

RESULTS

501,121 individuals died in 2015, 59% of whom were in a hospital at the time of death. The aggregated spending totaled 9% of total health expenditures, a mean of €28,085 per capita, 44% of which was spent during the last 3 months of life. Hospital admissions represented over 70% of total expenditures; 21.3% of the population used hospital palliative care services in their last year of life. Analyses performed on breast cancer, dementia and lung disease found that differences in care pathways markedly influenced spending and were not simply explained by patients characteristics.

CONCLUSION

Diagnoses and care trajectories, including repeated hospital stays, are the main drivers of the last year of life expenditures. Our data suggests that early identification of patients requiring palliative care and community-based end-of-life service delivery is feasible and could better support patients, families and caregivers with constant or reduced costs.

Investigation of the pharmacological treatment patterns of Chinese patients with major depressive disorder under real-world settings using multi-channel sequence analysis

Background

Despite many treatment guidelines available now, the treatment patterns of major depressive disorder (MDD) in China haven't been well-understood due to complexity and diversity.

Aim

To describe pharmacological treatment patterns of MDD patients in real-world settings using electronic health records from a major psychiatric hospital in China.

Methods

MDD patients (18-65 years, ICD-10: F32.x, F33.x) newly initiated single antidepressant (AD) in 2015 were enrolled, the date of first AD prescription during the study period was defined as index date, and eligible patients were followed up to 1 year. Treatment patterns were revealed and analyzed using multi-channel sequence analysis (MCSA), considering patients' chronological sequences (in days) of AD prescription, cumulative treatment step(s), and polypharmacy usage during the follow-up.

Results

This study (n = 5,003) identified four types of MDD treatment patterns. The first type (1-time treatment) represents the largest proportion of patients (73.6%, n = 3,686), followed by the second type (6-month consistent treatment) and third type (long-term, consistent treatment) collectively accounted for 20.6% (n = 1,031) of patients, by contrast the last type (long-term, inconsistent treatment) made up the rest 5.7% (n = 286) of patients while exhibiting the most complicated treatments patterns. The choice of AD was dominated by selective serotonin reuptake inhibitors (SSRIs), while treatment duration spent in polypharmacy spanned at 2.8%, 16.4%, 2.0%, and 36.5% over the four types, respectively.

Conclusion

Treatment patterns reflecting real-world pharmacological treatment practices of MDD in China were revealed using MCSA. The observed discrepancies between real-world practice and treatment guidelines provided additional insights in improving the clinical management of MDD.

Self-reported continuity and coordination of antenatal care and its association with obstetric near miss in Uasin Gishu county, Kenya

BACKGROUND

Continuity and coordination of care are core principles of high-quality primary health care. Optimising continuity and coordination improves maternal satisfaction. However, their association with morbidity and mortality outcomes is unclear. The obstetric near-miss approach can be used to investigate whether continuity and coordination influences the occurrence of a severe maternal outcome.

AIM

To compare self-reported continuity and coordination of care between obstetric near-miss survivors and those without near miss during pregnancy, delivery and postpartum.

SETTING

Uasin Gishu county, Rift Valley region, Kenya.

METHODS

A cross-sectional survey targeting 340 postnatal mothers. Continuity of care index (COCI) and modified continuity of care index (MCCI) were used to estimate longitudinal continuity. The Likert scale was administered to measure perceived continuity and coordination of care. Mann-Whitney U test and binomial logistic regression were used for hypothesis testing.

RESULTS

COCI and MCCI were lower among near-miss survivors (COCI = 0.80, p = 0.0026), (MCCI = 0.62, p = 0.034). Near-miss survivors scored lower on items assessing coordination between a higher-level provider and usual antenatal clinic (mean = 3.6, p = 0.006) and general coordination of care during pregnancy (mean = 3.9, p = 0.019). Presence of a non-life-threatening morbidity in pregnancy was associated with occurrence of near miss (aOR = 4.34, p = 0.001).

CONCLUSION

Near-miss survivors scored lower on longitudinal continuity and coordination of care across levels. Further research should focus on strengthening coordination, determining the optimal level of longitudinal continuity and improving systems for early identification and management of morbidities in pregnancy.Contribution: The results of this study show that while longitudinal and relational COC is important during the antenatal period, the presence of a non-life-threatening condition in pregnancy remains the most important predictor of the occurrence of a near miss.

Identifying common patterns of health services use: a longitudinal study of older Swiss adults' care trajectories

BACKGROUND

Population ageing puts pressure on health systems initially designed to handle acute and episodic illnesses. Segmenting an ageing population based on its healthcare utilization may enable policymakers to undertake evidence-based resource planning. We aimed to derive a typology of healthcare utilization trajectories in Swiss older adults.

METHODS

Our work used data from the Lc65 + study, a population-based cohort of individuals aged 65 to 70 years at enrolment. The dimensions of healthcare utilization considered were ambulatory care, emergency care, hospitalizations, professional home care and nursing home stay. We applied the Sequence Analysis framework, within which we quantified the variation between each multidimensional pair of sequences, implemented a clustering procedure that grouped together older persons with similar profiles of health services use, and characterized clusters of individuals using selected baseline covariates.

RESULTS

Healthcare utilization trajectories were analysed for 2271 community-dwelling older adults over a period of 11 years. Six homogeneous subgroups were identified: constant low utilization (83.3% of participants), increased utilization (4.9%), late health deterioration (4.4%), ambulatory care to nursing home (1.5%), early fatal event (3.8%) and high ambulatory care (2.1%). Associations were found between cluster membership and age, sex, household composition, self-perceived health, grip strength measurement, comorbidities, and functional dependency.

CONCLUSIONS

The heterogeneous healthcare utilization profiles can be clustered into six common patterns. Different manifestations of functional decline were apparent in two distinct trajectory groups featuring regular home care use. Furthermore, a small proportion of individuals with a unique set of characteristics was related to the highest levels of ambulatory and emergency care use. New research avenues are outlined to investigate time-varying effects of health factors inside the clusters containing most unfavourable outcomes.

Multidimensional analysis of adult patients’ care trajectories before a first diagnosis of schizophrenia

For patients at high-risk for developing schizophrenia, a delayed diagnosis could be affected, among many reasons, by their patterns of healthcare use. This study aims to describe and generate a typology of patients’ care trajectories (CTs) in the 2 years preceding a first diagnosis of schizophrenia, over a medico-administrative database of 3712 adults with a first diagnosis between April 2014 and March 2015 in Quebec, Canada. This study applied a multidimensional approach of State Sequence Analysis, considering together sequences of patients’ diagnoses, care settings and care providers. Five types of distinct CTs have emerged from this data-driven analysis: The type 1, shared by 77.6% of patients, predominantly younger men, shows that this group sought little healthcare, among which 17.5% had no healthcare contact for mental disorders. These individuals might benefit from improved promotion and prevention of mental healthcare at the community level. The types 2, 3 and 4, with higher occurrence of mental disorder diagnoses, represent together 19.5% of the study cohort, mostly middle-aged and women. These CTs, although displaying roughly similar profiles of mental disorders, revealed very dissimilar sequences and levels of care providers encounters, primary and specialized care use, and hospitalizations. Surprisingly, patients of these CTs had few consultations with general practitioners. An increased attentiveness for middle-aged patients and women with high healthcare use for mental disorders could help to reduce delayed diagnosis of schizophrenia. This calls for further consideration of healthcare services for severe mental illness beyond those offered to young adults.

Skilled antenatal care services utilisation in sub-Saharan Africa: a pooled analysis of demographic and health surveys from 32 countries

BACKGROUND

Each day, an estimated 800 women die from preventable pregnancy and childbirth related complications, where 99% of these avoidable deaths happen in low-and middle-income countries. Skilled attendance during antenatal care (ANC) plays a role in reducing maternal and child mortality. However, the factors that predict the utilisation of skilled ANC services in sub-Saharan Africa (SSA) remains sparsely investigated. Therefore, we examined women's utilisation of skilled ANC services in SSA.

METHODS

The research used pooled data from the most recent Demographic and Health Surveys conducted in 32 countries in SSA between January 1, 2010, and December 31, 2019. Binary logistic regression was used to examine the predictors of skilled ANC services utilisation. The results are presented as crude and adjusted odds ratios (aOR) with 95% confidence interval (CI).

RESULTS

The prevalence of skilled ANC services utilisation in SSA was 76.0%, with the highest and lowest prevalence in Gambia (99.2%) and Burundi (8.4%), respectively. Lower odds of ANC from skilled providers was found among women aged 45-49 compared to those aged 20-24 (aOR = 0.86, CI = 0.79-0.94); widowed women compared to married women (aOR = 0.84, CI = 0.72-0.99); women who consider getting permission to visit the health facility as a big problem compared to those who consider that as not a big problem (aOR = 0.74, CI = 0.71-0.77); women who consider getting money needed for treatment as not a big problem compared to those who consider that as a big problem (aOR = 0.84, CI = 0.72-0.99); and women who consider distance to the health facility as a big problem compared to those who consider that as not a big problem (aOR = 0.75, CI = 0.72-0.77).

CONCLUSION

SSA has relatively high prevalence of skilled ANC services utilisation, however, there are substantial country-level disparities that need to be prioritised. Increasing maternal reproductive age being widowed and far distance to health facility were factors that predicted lower likelihood of skilled ANC services utilisation. There is, therefore, the need to intensify female formal education, invest in community-based healthcare facilities in rural areas and leverage on the media in advocating for skilled ANC services utilisation.

Identifying and Investigating Ambulatory Care Sequences Before Invasive Coronary Angiography

BACKGROUND

The concept of care pathways is widely used to provide efficient, timely, and evidence-based medical care. Recently, the investigation of actual empirical patient pathways has gained attention. We demonstrate the usability of State Sequence Analysis (SSA), a data mining approach based on sequence clustering techniques, on comprehensive insurance claims data from Germany to identify empirical ambulatory care sequences. We investigate patients with coronary artery disease before invasive coronary angiography (CA) and compare identified patterns with guideline recommendations. This patient group is of particular interest due to high and regionally varying CA rates.

METHODS

Events relevant for the care of coronary artery disease patients, namely physician consultations and medication prescriptions, are identified based on medical guidelines and combined to define states. State sequences are determined for 1.5 years before CA. Sequence similarity is defined for clustering, using optimal matching with theory-informed substitution costs. We visualize clusters, present descriptive statistics, and apply logistic regression to investigate the association of cluster membership with subsequent undesired care events.

RESULTS

Five clusters are identified, the included patients differing with respect to morbidity, urbanity of residential area, and health care utilization. Clusters exhibit significant differences in the timing, structure, and extent of care before CA. When compared with guideline recommendations, 3 clusters show signs of care deficits.

CONCLUSIONS

Our analyses demonstrate the potential of SSA for exploratory health care research. We show how SSA can be used on insurance claims data to identify, visualize, and investigate care patterns and their deviations from guideline recommendations.

Identifying homogeneous healthcare use profiles and treatment sequences by combining sequence pattern mining with care trajectory clustering in kidney cancer patients on oral anticancer drugs: A case study

OBJECTIVE

We evaluated the ability of a coupled pattern-mining and clustering method to identify homogeneous groups of subjects in terms of healthcare resource use, prognosis and treatment sequences, in renal cancer patients beginning oral anticancer treatment.

METHODS

Data were retrieved from the permanent sample of the French medico-administrative database. We applied the CP-SPAM algorithm for pattern mining to healthcare use sequences, followed by hierarchical clustering on principal components (HCPC).

RESULTS AND CONCLUSION

We identified 127 individuals with renal cancer with a first reimbursement of an oral anticancer drug between 2010 and 2017. Clustering identified three groups of subjects, and discrimination between these groups was good. These clusters differed significantly in terms of mortality at six and 12 months, and medical follow-up profile (predominantly outpatient or inpatient care, biological monitoring, reimbursement of supportive care drugs). This case study highlights the potential utility of applying sequence-mining algorithms to a large range of healthcare reimbursement data, to identify groups of subjects homogeneous in terms of their care pathways and medical behaviors.

Referral trajectories in patients with vertigo, dizziness and balance disorders and their impact on health-related quality of life and functioning: results from the longitudinal multicenter study MobilE-TRA

Background

Due to reported barriers in the management of patients with vertigo, dizziness and balance problems (VDB), referral trajectories starting from primary care might be determined by other factors than medical necessity. The objective of this paper was to examine the impact of disease-related and other determinants on referral trajectories of older patients with VDB and to investigate, how these trajectories affect the patients’ functioning and health-related quality of life (HRQoL).

Methods

Data originate from the longitudinal multicenter study MobilE-TRA, conducted in two German federal states. Referrals to neurologists or ear-nose-throat (ENT) specialists were considered. Referral patterns were visualized using a state sequence analysis. Predictors of referral trajectories were examined using a multinomial logistic regression model. Linear mixed models were calculated to assess the impact of referral patterns on the patients’ HRQoL and functioning.

Results

We identified three patterns of referral trajectories: primary care physician (PCP) only, PCP and neurologist, and PCP and ENT. Chances of referral to a neurologist were higher for patients with a neurological comorbidity (OR = 3.22, 95%-CI [1.003; 10.327]) and lower for patients from Saxony (OR = 0.08, 95%-CI [0.013; 0.419]). Patients with a PCP and neurologist referral pattern had a lower HRQoL and lower functioning at baseline assessment. Patients with unspecific diagnoses also had lower functioning.

Conclusion

Referral trajectories were determined by present comorbidities and the regional healthcare characteristics. Referral trajectories affected patients’ HRQoL. Unspecific VDB diagnoses seem to increase the risk of ineffective management and consequently impaired functioning.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11060-8.

Machine Learning-Based Analysis of Treatment Sequences Typology in Advanced Non-Small-Cell Lung Cancer Long-Term Survivors Treated With Nivolumab

PURPOSE

Immune checkpoint inhibitors substantially changed advanced non-small-cell lung cancer (aNSCLC) management and can lead to long-term survival. The aims of this study were (1) to use a machine learning method to establish a typology of treatment sequences on patients with aNSCLC who were alive 2 years after initiating a treatment with anti-programmed death-ligand 1 monoclonal antibody nivolumab and (2) to describe the patients' characteristics according to the typology of treatment sequences.

MATERIALS AND METHODS

This retrospective observational study was based on data from the comprehensive French hospital discharge database for all patients with lung cancer with at least one line of platinum-based chemotherapy, starting nivolumab between January 1, 2015, and December 31, 2016, and alive 2 years after nivolumab treatment initiation. Patients were followed until December 31, 2018. A typology of most common treatment sequences was established using hierarchical clustering with time sequence analysis.

RESULTS

Two thousand two hundred twelve study patients were, on average, 63.0 years old, 69.9% of them were men, and 61.9% had a nonsquamous cell carcinoma. During the 2 years after nivolumab treatment initiation, clusters of patients with four basic types of treatment sequences were identified: (1) almost continuous nivolumab treatment (44% of patients); (2) nivolumab most of the time followed by a treatment-free interval or a chemotherapy (15% of patients); and a short or medium nivolumab treatment, followed by (3) a long systemic treatment-free interval (17% of patients) or (4) a long chemotherapy (23% of patients).

CONCLUSION

This machine learning approach enabled the identification of a typology of four representative treatment sequences observed in long-term survival. It was noted that most long-term survivors were treated with nivolumab for well over 1 year.

Use of State Sequence Analysis in Pharmacoepidemiology: A Tutorial

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.

Care consumption of people with multiple sclerosis: A multichannel sequence analysis in a population-based setting in British Columbia, Canada

BACKGROUND

Persons with multiple sclerosis (PwMS) typically require complex multidisciplinary care, which is rarely formally assessed.

OBJECTIVES

We applied multichannel sequence analysis (MCSA) to identify care consumption patterns by PwMS in British Columbia, Canada.

METHODS

We created two cohorts, comprising incident and prevalent MS cases, using linked clinical and administrative data. We applied MCSA to quantify and compare the care pathways of PwMS, based on all-cause hospitalizations and physician visits (divided into five specialities). Care consumption clusters were characterized using demographic and clinical features.

RESULTS

From 1048 incident and 3180 prevalent PwMS, the MCSA identified 12 and 6 distinct care consumption clusters over a median follow-up of 9.6 and 13.0 years, respectively. Large disparities between clusters were observed; the median number of annual consultations ranged from 5.6 to 21.3 for general practitioners, 1.2 to 4.6 for neurologists and 0 to 5.3 for psychiatrists in the incident cohort. Characteristics at MS symptom onset associated with the highest care consumption included high comorbidity burden and older age. There were similar disparities and associations for prevalent PwMS.

CONCLUSION

The distinct patterns of care consumption, which were reminiscent of the heterogeneity of MS itself, may facilitate health service planning and evaluation, and provide a novel outcome measure in health research.

Analysis of Trajectories of Care After Bariatric Surgery Using Data Mining Method and Health Administrative Information Systems

CONTEXT

The 30-day readmission rate after bariatric surgery is considered an important metric of the quality of hospital care. However, readmission rate beyond 30 days is rarely reported and does not provide any information about trajectories of care which would be of great interest for healthcare planning. The aim of this study was to analyze trajectories of care during the first year after bariatric surgery on a nationwide basis using data mining methods.

METHOD

This was a retrospective descriptive study on the trajectories of care within the first year after bariatric surgery. Data were extracted from a national administrative claims database (the PMSI database) and trajectories were defined as principal diagnosis of successive readmissions. Formal Concept Analysis was performed to find common concepts of trajectories of care.

RESULTS

We included for analysis 198,389 bariatric procedures performed on 196,323 patients. Twelve main concepts were selected. About one third of patients (32.4%) were readmitted in the first year after surgery. Most common trajectories were as follows: regular follow-up (14.9%), cholelithiasis (2.2%), abdominal pain (1.9%), and abdominal sepsis (1.3%). Important differences were found in trajectories among different bariatric procedures: 1.8% of gastric banding patients had pregnancy-related events (delivery or medical abortion), while we observed a readmission rate for abdominal sepsis in 2.7% and 5.1% of patients operated of gastric bypass and sleeve gastrectomy respectively.

CONCLUSION

Administrative claim data can be analyzed through Formal Concept Analysis in order to classify trajectories of care. This approach permits to quantify expected postoperative complications and to identify unexpected events.

Change in the regulatory framework for zolpidem: What is the impact on the landscape of the prescription of sedative medications? The French national ZORRO study

AIMS

In recent years, zolpidem has been the subject of numerous reports of misuse, abuse and dependence. In view of these risks, the French drug agency (ANSM) decreed in April 2017 the implementation of secure prescription pads. The objective of this study was to evaluate the impact of this regulatory measure on the prescription of zolpidem and other sedative medications (zopiclone, benzodiazepines and antihistamines) in long-term users of zolpidem and associated factors.

METHODS

We performed a historical cohort study using data from the Generalist Sample of Beneficiaries (EGB). All patients aged over 18 years old who were long-term users (at least 3 months) before the measure were enacted. We analysed the reimbursement trajectories of zolpidem, zopiclone, benzodiazepines and antihistamines (hydroxyzine and alimemazine) up to 2 years after the measure using a state sequence analysis.

RESULTS

Overall, 2502 patients were analysed. A four-cluster typology was identified: continuation of zolpidem (n = 1044, 42%), discontinuation of sedative medications (n = 766, 31%), change to zopiclone (n = 537, 21%) and change to hypnotic benzodiazepines (n = 155, 6%). The most frequently prescribed hypnotic benzodiazepine was lormetazepam. We identified age, sex, treatment for psychiatric or addictive disorder and volume of zolpidem use before the measure as factors associated with different reimbursement trajectories after the regulatory change.

CONCLUSION

The regulatory change for zolpidem prescriptions reduced exposure to zolpidem among long-term users and also had a broad impact on prescriptions of other sedative medications. Switching to other medications that also present a potential risk of abuse or dependence should be carefully monitored.

Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches

Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.

Longitudinal continuity of care during antenatal and delivery in the Volta Region of Ghana

OBJECTIVE

To determine the extent of longitudinal continuity of care (CoC) during pregnancy and delivery in the Volta Region of Ghana.

METHODS

Longitudinal data were used from the National Health Insurance Claims Dataset for the period January to December 2013 for pregnant women who sought antenatal and delivery care in the region. Pregnant women who delivered at a health facility with at least three visits were included in the study. Five CoC indices were calculated for each pregnant woman.

RESULTS

Of the 14 474 pregnant women included in the study, 58.4% had perfect CoC. Mean CoC indices were: most frequent provider continuity (MFPC) 0.82 ± 0.25; modified, modified continuity index (MMCI) 0.86 ± 0.20; continuity of care index (COCI) 0.76 ± 0.30; sequential continuity index (SECON) 0.80 ± 0.28; and place of delivery continuity (PDC) 0.68 ± 0.41.

CONCLUSION

There are relatively medium to high levels of CoC indices during pregnancy and delivery, with place of delivery CoC having the lowest score, an indication that more pregnant women switched providers during delivery. There is a need for policy to ensure CoC during pregnancy.

Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories

Background

Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts.

Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients’ characteristics for each CT type.

Methods

This is an observational cohort study extracted from Quebec’s medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a ‘tailored’ multidimensional State Sequence Analysis, based on the “6W” model of Care Trajectories. Three dimensions were considered: the care setting (“where”), the reason for consultation (“why”), and the speciality of care providers (“which”). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients’ characteristics using the usual descriptive statistics.

Results

The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization.

Conclusions

The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

The French Pregnancy Cohort: Medication use during pregnancy in the French population

Purpose

We described the medication use during pregnancy in the French population using the French Pregnancy Cohort (FPC).

Methods

The FPC was built with the sampling of all pregnant women included in the French Echantillon généraliste des bénéficiaires (EGB), which is a 1/97^th representative sample of the population covered by the French health insurance. The EGB includes anonymized information on the socio-demographic and medical characteristics of beneficiaries, and the health care services they have received such as diagnoses and procedure codes as well as data on filled reimbursed medication; EGB also includes data on hospital stays in all public and private French health facilities. Each filled prescription record contains information on drug brand and generic names, date of prescription and date of dispensing, quantity dispensed, mode of administration, duration of prescription, dosage, and prescribing physician specialty. FPC includes data on all pregnancies of women in the EGB (2010–2013). Date of entry in the FPC is the first day of pregnancy regardless of pregnancy outcome (spontaneous abortions or planned abortions (with or without medical reasons), deliveries), and data on women are collected retrospectively for a period of one year before pregnancy, and prospectively during pregnancy, and up to one year after delivery. The prevalence of prescribed medications before, during and after pregnancy was compared; comparison was also done between trimesters. Pregnancy outcomes are described and include spontaneous and planned abortions, livebirths, and stillbirths.

Results

FPC includes data on 36,065 pregnancies. Among them, 27,253 (75.6%) resulted in a delivery including 201 stillbirths (0.7%). The total number of spontaneous abortions was 6,718 (18.6%), and planned abortions 2,094 (5.8%). The prevalence of filled medication use was 91.1%, 89.9%, and 95.6% before, during and after pregnancy, respectively. Although there was a statistically significant decrease in the proportion of use once the pregnancy was diagnosed (first trimester exposure, 76.4% vs. exposure in the year prior to pregnancy, 91.1% (p < .01)), post-pregnancy medication use was above the pre-pregnancy level (95.6%). Maternal depression was the most prevalent comorbidity during pregnancy (20%), and post-partum depression was higher in those who delivered a stillborn infant (38.8%) as well as in those with a spontaneous (19.5%) or planned abortion (22.4%) compared to those with a liveborn (12.0%).

Conclusion

FPC is an excellent tool for the study of the risk and benefit of drug use during the perinatal period. FPC has the advantage of including a representative sample of French pregnant women, and study medications only available in France in addition to others available worldwide.

Mining clinical big data for drug safety: Detecting inadequate treatment with a DNA sequence alignment algorithm

Health data mining can bring valuable information for drug safety activities. We developed a visual analytics tool to find specific clinical event sequences within the data contained in a clinical data warehouse. To this aim, we adapted the Smith-Waterman DNA sequence alignment algorithm to retrieve clinical event sequences with a temporal pattern from the electronic health records included in a clinical data warehouse. A web interface facilitates interactive query specification and result visualization. We describe the adaptation of the Smith-Waterman algorithm, and the implemented user interface. The evaluation with pharmacovigilance use cases involved the detection of inadequate treatment decisions in patient sequences. The precision and recall results (F-measure = 0.87) suggest that our adaptation of the Smith-Waterman-based algorithm is well-suited for this type of pharmacovigilance activities. The user interface allowed the rapid identification of cases of inadequate treatment.

Determinants of choice of skilled antenatal care service providers in Ghana: analysis of demographic and health survey

BACKGROUND

The International Safe Motherhood initiative provides a focus for programmes and research to improve maternal health in low - income countries. Antenatal care is one of the key pillars of the initiative. This study sought to examine the association between background characteristics and choice of skilled providers of antenatal care services in Ghana.

METHODS

The study used data from the six rounds of the Ghana Demographic and Health Survey (GDHS). Binary logistic regression models were applied to analyse the data.

RESULTS

Results show that the proportion of women who received antenatal care (ANC) services from skilled providers improved over the period. Also, women with secondary education (OR = 1.42, CI = 1.07-1.88), richest wealth status (OR = 5.10, CI = 2.28-11.85) were more likely to utilise antenatal care services from skilled providers. Whereas women from rural areas (OR = 0.55, CI = 0.41-0.74), with four births or more (OR = 0.55, CI = 0.36-0.85) and from the northern ethnic group were less likely to utilise antenatal care services from skilled providers.

CONCLUSION

Choice of skilled providers of antenatal care services were predicted by some predisposing factors including education, ethnicity, and ecological zone. Also enabling factors such as wealth status, residence and the need for care factor, parity predicted choice of skilled providers of antenatal care services. Women with secondary or higher education, those within richer and richest wealth status, those from forest zone are more likely to utilise the services of skilled providers during their antenatal care visits. Whereas women from rural areas, those with four births or more and those with the northern ethnic group were more likely to utilise ANC service from unskilled providers. The Ghana Health Service and Ministry of Health should encourage women in rural areas to utilise antenatal care services from skilled providers through social and behaviour change communication campaigns.

Development of an algorithm to identify pregnancy episodes and related outcomes in health care claims databases: An application to antiepileptic drug use in 4.9 million pregnant women in France

Purpose

Access to claims databases provides an opportunity to study medication use and safety during pregnancy. We developed an algorithm to identify pregnancy episodes in the French health care databases and applied it to study antiepileptic drug (AED) use during pregnancy between 2007 and 2014.

Methods

The algorithm searched the French health care databases for discharge diagnoses and medical procedures indicative of completion of a pregnancy. To differentiate claims associated with separate pregnancies, an interval of at least 28 weeks was required between 2 consecutive pregnancies resulting in a birth and 6 weeks for terminations of pregnancy. Pregnancy outcomes were categorized into live births, stillbirths, elective abortions, therapeutic abortions, spontaneous abortions, and ectopic pregnancies. Outcome dates and gestational ages were used to calculate pregnancy start dates.

Results

According to our algorithm, live birth was the most common pregnancy outcome (73.9%), followed by elective abortion (17.2%), spontaneous abortion (4.2%), ectopic pregnancy (1.1%), therapeutic abortion (1.0%), and stillbirth (0.4%). These results were globally consistent with French official data. Among 7 559 701 pregnancies starting between 2007 and 2014, corresponding to 4 900 139 women, 6.7 per 1000 pregnancies were exposed to an AED. The number of pregnancies exposed to older AEDs, comprising the most teratogenic AEDs, decreased throughout the study period (−69.4%), while the use of newer AEDs increased (+73.4%).

Conclusions

We have developed an algorithm that allows identification of a large number of pregnancies and all types of pregnancy outcomes. Pregnancy outcome and start dates were accurately identified, and maternal data could be linked to neonatal data.

Categorical state sequence analysis and regression tree to identify determinants of care trajectory in chronic disease: Example of end-stage renal disease

BACKGROUND

Patients with chronic diseases, like patients with end-stage renal disease (ESRD), have long history of care driven by multiple determinants (medical, social, economic, etc.). Although in most epidemiological studies, analyses of health care determinants are computed on single health care events using classical multivariate statistical regression methods. Only few studies have integrated the concept of treatment trajectories as a whole and studied their determinants.

METHODS

All 18- to 80-year-old incident ESRD patients who started dialysis in Ile-de-France or Bretagne between 2006 and 2009 and could be followed for a period of 48 months after initiation of a renal replacement therapy were included (n = 5568). Their care trajectories were defined as categorical state sequences. Associations between patients' characteristics and care trajectories were assessed using a regression tree model together with a discrepancy analysis.

RESULTS

On average, each patient experienced 1.56 different renal replacement therapies (min = 1; max = 5) during the 48 months of follow-up. About 55% of patients never changed treatment and only 1% tried three or more renal replacement therapy modalities. Twelve homogeneous care trajectory groups were identified. Covariates explained 12% of the discrepancy between groups, particularly age, regions and initiation of hemodialysis with a catheter.

CONCLUSIONS

Regression tree analysis of categorical state sequence highlighted geographical disparities in the care trajectory of French patients with ESRD that cannot be observed when focusing on a single outcome, such as survival. This method is an original tool to visualize and characterize care trajectories, notably in the context of chronic condition like ESRD.

Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis

The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. We here propose an innovative method in epidemiology that is derived from social sciences: state sequence analysis (SSA). This method takes into account the chronology of care consumption and allows for identification of specific patterns. A process for using SSA in the health area is proposed and discussed. The main steps are: data coding, measurement of dissimilarities between sequences (focusing on optimal matching methods and the choice of related costs), and application of a clustering method to obtain a typology of sequence patterns. As an example of its use in the health area, SSA was employed to analyse care pathways of a random sample of patients with multiple sclerosis. This sample has been selected from the main French healthcare database covering the period 2007 to 2013 (n = 1 000). A five-cluster typology was obtained which allowed distinction of care consumption groups. Overall, about half of the patients had low care consumption, about one quarter had medium to high consumption, and another quarter had high consumption. We conclude that state sequence analysis is an innovative and flexible methodology that is worth considering in health care research.

The '6W' multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions

OBJECTIVES

To synthesize concepts and approaches related to the analysis of patterns or processes of care and patient's outcomes into a comprehensive model of care trajectories, focusing on hospital readmissions for patients with chronic ambulatory care sensitive conditions (ACSCs).

STUDY DESIGN

Narrative literature review.

METHODS

Published studies between January 2000 and November 2017, using the concepts of 'continuity', 'pathway', 'episode', and 'trajectory', and focused on readmissions and chronic ACSCs, were collected in electronic databases. Qualitative content analysis was performed with emphasis on key constituents to build a comprehensive model.

RESULTS

Specific common constituents are shared by the concepts reviewed: they focus on the patient, aim to measure and improve outcomes, follow specific periods of time and consider other factors related to care providers, care units, care settings, and treatments. Using these common denominators, the comprehensive '6W' multidimensional model of care trajectories was created. Considering patients' attributes and their chronic ACSCs illness course ('who' and 'why' dimensions), this model reflects their patterns of health care use across care providers ('which'), care units ('where'), and treatments ('what'), at specific periods of time ('when').

CONCLUSIONS

The '6W' model of care trajectories could provide valuable information on 'missed opportunities' to reduce readmission rates and improve quality of both ambulatory and inpatient care.

Impact of a New York City supportive housing program on Medicaid expenditure patterns among people with serious mental illness and chronic homelessness

BACKGROUND

A rapid increase of Medicaid expenditures has been a serious concern, and housing stability has been discussed as a means to reduce Medicaid costs. A program evaluation of a New York City supportive housing program has assessed the association between supportive housing tenancy and Medicaid savings among New York City housing program applicants with serious mental illness and chronic homelessness or dual diagnoses of mental illness and substance use disorder, stratified by distinctive Medicaid expenditure patterns.

METHODS

The evaluation used matched data from administrative records for 2827 people. Sequence analysis identified 6 Medicaid expenditure patterns during 2 years prior to baseline among people placed in the program (n = 737) and people eligible but not placed (n = 2090), including very low Medicaid coverage, increasing Medicaid expenditure, low, middle, high, and very high Medicaid expenditure patterns. We assessed the impact of the program on Medicaid costs for 2 years post-baseline via propensity score matching and bootstrapping.

RESULTS

The housing program was associated with Medicaid savings during 2 years post-baseline (-$9526, 95% CI = -$19,038 to -$2003). Stratified by Medicaid expenditure patterns, Medicaid savings were found among those with very low Medicaid coverage (-$15,694, 95% CI = -$35,926 to -$7983), increasing Medicaid expenditures (-$9020, 95% CI = -$26,753 to -$1705), and high Medicaid expenditure patterns (-$14,450, 95% CI = -$38,232 to -$4454). Savings were largely driven by shorter psychiatric hospitalizations in the post-baseline period among those placed.

CONCLUSIONS

The supportive housing program was associated with Medicaid savings, particularly for individuals with very low Medicaid coverage, increasing Medicaid expenditures, and high Medicaid expenditures pre-baseline.

Supply kits for antenatal and childbirth care: a systematic review

INTRODUCTION

It is critical to increase the uptake of interventions proven to be effective to improve maternal and perinatal outcomes. Supply kits have been suggested to be a feasible strategy designed to ensure timely availability and effective follow-up of care.

OBJECTIVE

We conducted a systematic review to summarize the evidence on the uptake, effectiveness and safety of supply kits for maternal care.

SEARCH STRATEGY

MEDLINE, the Cochrane Pregnancy and Childbirth Group's Trials Register, Campbell Collaboration, Lilacs, Embase and unpublished studies were searched.

SELECTION CRITERIA

Studies that reported the efficacy, safety and use of supply kits for maternal healthcare were eligible. Participants were pregnant women or in childbirth. Supply kits were defined as a collection of medicines, supplies or instruments packaged together with the aim of conducting a healthcare task.

DATA COLLECTION AND ANALYSIS

Two reviewers independently performed the screening, data extraction, and methodological and quality assessment.

MAIN RESULTS

24 studies were included: 4 of them were systematic reviews and 20 primary studies. Eighteen studies evaluated a so-called "clean delivery kit". In all but two studies, the kits were used by more than half of the participants. A meta-analysis was deemed inappropriate due to the heterogeneity in study design, in the components of the interventions implemented, in the content of the kits, and in outcomes. Nine studies assessed neonatal outcomes and found statistically significant reductions in cord infection, sepsis and tetanus-related mortality in the intervention group. Three studies showed evidence of reduced neonatal mortality (OR 0.52, 0.60 and 0.71) with statistically significant confidence intervals in all cases. Four studies reported odd ratios for maternal mortality, but only one showed evidence of a statistically significant decrease in this outcome but it was ascribed to hand washing prior to childbirth and not with the use of kits.

CONCLUSION

This review suggests potential benefits in the use of supply kits to improve maternal and neonatal health. However, the observational nature of the studies, the heterogeneity and the use of kits incorporated within complex interventions limit the interpretation of the findings.

Providers of antenatal care services in Ghana: evidence from Ghana demographic and health surveys 1988-2014

Background

Antenatal care is one of the three most essential care - antenatal, delivery and post-natal, given to women during pregnancy and has the potential to contribute towards the achievement of the Sustainable Development Goal (SDG) target 3.1- reducing the global maternal mortality ratio to less than 70 per 100,000 and target 3.8 – achieve universal health coverage. The main objective is to examine the contribution of the various providers of antenatal care services in Ghana from 1988 to 2014.

Methods

The study uses data from all the six rounds of the Ghana Demographic and Health Survey (GDHS). Binary logistic regression models were applied to examine the association between background characteristics of respondents and providers of antenatal care services.

Results

The results show that majority of antenatal care services were provided by nurses over the period under review. The proportion of women who received antenatal care services from nurses improved over the period from 55% in 1988 to 89.5% in 2014. Moreover, there was a decline in antenatal care services provided by traditional birth attendants and women who did not receive antenatal care services from any service provider over the years under review. It was observed that women from rural areas were more likely to utilise antenatal care services provided by traditional birth attendants, whilst those from urban areas were more likely to utilise antenatal care from doctors and nurses.

Conclusion

To further improve access to and utilisation of antenatal care services provided by nurses and doctors it is recommended that the Ghana Health Service and the Ministry of Health should put in place systems aimed at improving on the quality of care given such as regular training workshops for health personnel and assessment of patient’s satisfaction with services provided. Also, they should encourage women in rural areas especially those from the savannah zone to utilise antenatal care services from skilled providers through social and behaviour change communication campaigns.

Measuring care trajectories using health administrative databases: a population-based investigation of transitions from emergency to acute care

Background

A patient’s trajectory through the healthcare system affects resource use and outcomes. Data fields in population-based administrative health databases are potentially valuable resources for constructing care trajectories for entire populations, provided they can capture patient transitions between healthcare services. This study describes patient transitions from the emergency department (ED) to other healthcare settings, and ascertains whether the discharge disposition field recorded in the ED data was a reliable source of patient transition information from the emergency to the acute care settings.

Methods

Administrative health databases from the province of Saskatchewan, Canada (population 1.1 million) were used to identify patients with at least one ED visit to provincial teaching hospitals (n = 5) between April 1, 2006 and March 31, 2012. Discharge disposition from ED was described using frequencies and percentages; and it includes categories such as home, transfer to other facilities, and died. The kappa statistic with 95 % confidence intervals (95 % CIs) was used to measure agreement between the discharge disposition field in the ED data and hospital admission records.

Results

We identified N = 1,062,861 visits for 371,480 patients to EDs over the six-year study period. Three-quarters of the discharges were to home, 16.1 % were to acute care in the same facility in which the ED was located, and 1.6 % resulted in a patient transfer to a different acute care facility. Agreement between the discharge disposition field in the ED data and hospital admission records was good when the emergency and acute care departments were in the same facility (κ = 0.77, 95 % CI 0.77, 0.77). For transfers to a different acute care facility, agreement was only fair (κ = 0.36, 95 % CI 0.35, 0.36).

Conclusions

The majority of patients who attended EDs did not transition to another healthcare setting. For those who transitioned to acute care, accuracy of the discharge disposition field depended on whether the two services were provided in the same facility. Using the hospital data as reference, we conclude that the discharge disposition field in the ED data is not reliable for measuring transitions from ED to acute care.