Perceived Health Service, Quality of Care, and Multidrug Resistent Tuberculosis: A Case-Control Study in Central Java, Indonesia

Background: Tuberculosis (TB) remains a leading cause of morbidity and mortality in developing countries, including Indonesia. Drug resistance, in combination with other factors, results in even more increased morbidity and mortality due to tuberculosis. Globally, there were about 0.5 million cases of multidrug resistant tuberculosis (MDR-TB). The WHO reported an alarming rise of not only MDR-TB but also of extreme drug-resistant tuberculosis (XDR-TB) globally. This study aimed to determine the associations of perceived health care behavior and perceived quality of care with MDR-TB in Central Java, Indonesia. Subjects and Method: This was a case-control study conducted in Surakarta, Central Java, Indonesia, from August 2017 to January 2018. A sample of 309 subjects was selected for this study, consisting of 81 MDR-TB cases and 228 non MDR-TB controls. The dependent variable was MDR-TB. The independent variables were perceived health provider behavior and perceived quality of care. MDR-TB data were obtained from medical record. The other variables were collected by questionnaire. The data were analyzed by a multiple logistic regression. Results: The risk of MDR-TB increased with unfavorable perceived provider behavior (OR= 2.80; 95% CI= 1.64 to 5.09; p<0.001) and perceived poor quality of health service (OR= 1.90; 95% CI= 1.15 to 3.37; p= 0.013) received by the patients. Conclusion: The risk of MDR-TB is associated with unfavorable perceived provider behavior and perceived poor quality of health service received by the patients. Keywords: perceived provider behavior, perceived quality of health service, MDR-T

Peningkatan Kemampuan dan Ketrampilan Inputing Data Tuberkulosis Bagi Programmer Tb

Petugas Tuberkulosis atau yang dikenal sebagai Programmer TB mempunyai peranan penting dalam pengelolaan data TB, termasuk di Kabupaten Sukoharjo, Jawa Tengah, ditemukannya capaian CNR BTA positif Kabupaten Sukoharjo tahun 2017 berada pada urutan terendah ke-3 setelah Kabupaten Semarang dan Kabupaten Magelang. Untuk mendukung pengembangan sistem informasi pendukung kebijakan Program TB maka diperlukan kegiatan untuk pelatihan penginputan data Tuberkulosis ke dalam SPK-TB atau Sistem Pendukung Keputusan Tuberkulosis oleh programmer TB yang secara langsung berhubungan dengan pasien TB di lapangan.Tujuan dari pengabdian ini adalah meningkakan kemampuan dan ketrampilan Prorammer TB di puskesmas untuk melakukan inputing data Tuberkulosis dengan menggunakan SPK-TB. Metode pelaksanaan kegiatan meliputi analisis masalah dari mitra untuk menentukan masalahnya, identifikasi karakteristik Programmer TB puskesmas dengan membagikan kuesioner, analisis kebutuhan dan keputusan sistem dengan mengidentifikasi kebutuhan yang diperlukan Programmer TB, Sosisalisasi penggunaan SPK-TB pada peserta dan pelatihan pencatatan atau pengunpulan data TB puskesmas. Adapun peserta yang mengikuti kegiatan ini sebanyak 12 Programmer TB dari berbagai puskesmas di wilayah kerja Kabupaten Sukoharjo, Jawa Tengah. Kegiatan ini menunjukkan adanya peningkatan kemampuan dan ketrampilan dari Programmer TB dalam melakukan inputing data dengan benar. Sehingga perlunya dukungan dari pihak Dinas Kesehatan Kabupaten Sukoharjo untuk menggunakan SPK-TB dalam inputing data TB

Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city’s center and low in the outskirts. This contrasts with the city’s population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. ‘What-if’ scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal

Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city&rsquo;s center and low in the outskirts. This contrasts with the city&rsquo;s population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. &lsquo;What-if&rsquo; scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal