Investigating the relationship between community pharmacy and GP Emergency Hormonal Contraception (EHC) provision: a linear regression analysis
Notice bibliographique
Résumé
Abstract Introduction Emergency Hormonal Contraception (EHC) is contracted by Local Authorities to be provided free-of-charge from 46% of community pharmacies in England. (1) There is no difference in EHC consultation outcomes between community pharmacy and GP surgeries. A study in a rural area demonstrated that introduction of a community pharmacy EHC service can reduce GP EHC prescribing rates by approximately 41%, without influencing Family Planning Clinics or Accident and Emergency departments. (2) However, it is not known whether this relationship is universally present, and this relationship has not previously been quantified. Aim To describe the relationship between rates of GP EHC prescribing and commissioned community pharmacy EHC provision. Methods Freedom of Information (FOI) requests were submitted to all Local Authorities in England for numbers of EHC provisions through commissioned services between March 2019 and April 2020. The data were matched to Clinical Commissioning Group (CCG) GP prescribing data, obtained from openprescribing.net. Using population estimates from the Office of National Statistics, rates of supply per 10,000 female population (aged 12-55) were determined. The data indicated small numbers of outliers, which can distort linear regression; boxplots allowed the removal of data points outside 1.5 times the Inter Quartile Range from the 1st or 3rd quarter. Using SPSS v24, linear regressions were calculated between GP prescribing rates and community pharmacy EHC provision rates. This was repeated for community pharmacy EHC provision rates and the proportion of commissioned pharmacies. Results There were 147 Local Authority commissioners identified across England, 113 (76.9%) responded to the FOI request. Of these, 5 did not commission EHC services from community pharmacy. Local Authority and CCG boundaries were compared, 86 areas were identified as ‘co-terminus’ (i.e., greater than 95% overlap). These 86 areas included 82,822 GP prescriptions and 207,731 community pharmacy provisions. The data reflected an estimated female population aged 12-55 of 9,380,153 (Local Authority mean 109,072, SD 83,899), 60% of the total English female (12-55) population. Removing outliers left 92.5% of the data for analysis. The mean GP prescribing rate was 79.3/10,000 (SD 26.3) and the mean community pharmacy provision rate was 200.2/10,000 (SD 154.9). Linear regression indicated a negative correlation between GP prescribing rates and community pharmacy provision rates (R2=0.21) and a positive correlation between community pharmacy provision rates and the proportion of commissioned pharmacies (R2=0.21). Conclusion This study shows that increasing the community pharmacy provision rate by 100/10,000 decreases the GP prescribing rate by 8/10,000. Increasing the proportion of commissioned pharmacies to 100%, through a national service may change GP prescribing rates. This regression analysis predicts this would decrease the GP EHC prescribing rate by 15% to 66.3/10,000. Whilst this data is not fully representative of commissioning in England, this single commissioning change could move 20,706 GP consultations to community pharmacy annually across England. Comparisons with Wales and Scotland (who have national services) suggest this impact could potentially even be doubled. The strength of this study is it’s use of routine data facilitating replication, however local commissioning arrangements mean the conclusions are not necessarily applicable beyond England. References (1) Mackridge AJ, Gray NJ, Krska J. A cross-sectional study using freedom of information requests to evaluate variation in local authority commissioning of community pharmacy public health services in England. BMJ Open. 2017;7(7):e015511 (2) Lloyd K, Gale E. Provision of emergency hormonal contraception through community pharmacies in a rural area. J Fam Plann Reprod Health Care. 2005;31(4):297-300.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».